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Control in an Information Rich World 

Report of the Panel on Future Directions in 
Control, Dynamics, and Systems 

30 June 2002 


The field of control provides the principles and methods used to design engineering 
systems that maintain desirable performance by automatically adapting to changes 
in the environment. Over the last forty years the field has seen huge advances, lever- 
aging technology improvements in sensing and computation with breakthroughs in 
the underlying principles and mathematics. Control systems now play critical roles 
in many fields, including manufacturing, electronics, communications, transporta- 
tion, computers and networks, and many military systems. 

As we begin the 21st Century, the opportunities to apply control principles and 
methods are exploding. Computation, communication and sensing are becoming 
increasingly inexpensive and ubiquitous, with more and more devices including 
embedded processors, sensors, and networking hardware. This will make possible 
the development of machines with a degree of intelligence and reactivity that will 
influence nearly every aspect of life on this planet, including not just the products 
available, but the very environment in which we live. 

New developments in this increasingly information rich world will rec^uire a 
significant expansion of the basic tool sets of control. The complexity of the control 
ideas involved in the operation of the Internet, semi- autonomous command and 
control systems, and enterprise-wide supply chain management, for example, are 
on the boundary of what can be done with available methods. Future applications 
in aerospace and transportation, information and networks, robotics and intelligent 
machines, biology and medicine, and materials and processing will create systems 
that are well beyond our current levels of complexity, and new research is required 
to enable such developments. 

The purpose of this report is to spell out some of the prospects for control 
in the current and future technological environment, to describe the role the field 
will play in military, commercial, and scientific applications over the next decade, 
and to recommend actions required to enable new breakthroughs in engineering and 
technology through application of control research. 

Panel Membership 

Richard M. Murray (chair) 
California Institute of Technology 

Karl J. Astrom Praniod P. Khargonekar 

Lund Institute of Technology University of Florida 

Stephen P. Boyd P. R. Kumar 

Stanford University University of Illinois 

Siva S. Banda P. S. Krishnaprasad 

Air Force Research Laboratory University of Maryland 

Roger W. Brockett Greg J. McRae 

Harvard University Massachusetts Institute of Technology 

John A. Burns Jerrold E. Marsden 

Virginia Tech California Institute of Technology 

Munzer A. Dahleh George Meyer 

Massachusetts Institute of Technology NASA Ames Research Center 

John C. Doyle William F. Powers 

California Institute of Technology Ford Motor Company 

John Guckenheimer Gunter Stein 

Cornell University Honeywell International 

Charles J. Holland Pravin Varaiya 

Department of Defense University of California, Berkeley 

Additional Contributors 

Richard Albanese, Jim Batterson, Richard Braatz, Dennis Bernstein, Joel 

Burdick, Raffaello D'Andrea, Michael Dickinson, Frank Doyle, Martha Gallivan, 

Jonathan How, Marc Jacobs, Jared Leadbetter, Jesse Leitner, Steven Low, Hideo 

Mabuchi, Dianne Newman, Shankar Sastry, John Seinfeld, Eduardo Sontag, Anna 

Stefanopoulou, Allen Tannenbaum, Claire Tomlin, Kevin Wise 



Preface vii 

1 Executive Summary 1 

2 Overview of the Field 7 

2.1 What is Control? 7 

2.2 Control System Examples 13 

2.3 The Increasing Role of Information-Based Systems 18 

2.4 Opportunities and Challenges Facing the Field 20 

3 Applications, Opportunities, and Challenges 27 

3.1 Aerospace and Transportation 29 

3.2 Information and Networks 39 

3.3 Robotics and Intelligent Machines 49 

3.4 Biology and Medicine 58 

3.5 Materials and Processing 65 

3.6 Other Applications 72 

4 Education and Outreach 79 

4.1 Tlie New Environment for Control Education 79 

4.2 Making Control More Accessible 81 

4.3 Broadening Control Education 83 

4.4 The Opportunities in K-12 Math and Science Education .... 84 

4.5 Other Opportunities and Trends 85 

5 Recommendations 89 

5.1 Integrated Control, Computation, Communications 89 

5.2 Control of Complex Decision Systems 90 

5.3 High-Risk, Long-Range Applications of Control 91 

5.4 Support for Theory and Interaction with Mathematics 92 

5.5 New Approaches to Education and Outreach 93 

5.6 Concluding Remarks 94 

A NSF/CSS Workshop on Education 97 

vi Contents 

Bibliography 101 

Index 105 


This report documents the findings and recommendations of the Panel on 
Future Directions in Control, Dynamics, and Systems. This committee was formed 
in April 2000 under initial sponsorship of the Air Force Office of Scientific Research 
(AFOSR) to provide a renewed vision of future challenges and opportunities in 
the field, along with recommendations to government agencies, universities, and 
research organizations to ensure continued progress in areas of importance to the 
industrial and defense base. The intent of this report is to raise the overall visibility 
of research in control, highlight its importance in applications of national interest, 
and indicate some of the key trends that are important for continued vitality of the 

The Panel was chaired by Professor Richard Murray (Caltech) and was formed 
with the help of an organizing committee consisting of Professor Roger Brock- 
ett (Harvard), Professor John Burns (VPI), Professor John Doyle (Caltech) and 
Dr. Gunter Stein (Honeywell). The remaining Panel members are Karl Astrom 
(Lund Institute of Technology), Siva Banda (Air Force Research Lab), Stephen 
Boyd (Stanford), Munzer Dahleh (MIT), John Guckenheimer (Cornell), Charles 
Holland (DDR&E), Pramod Khargonekar (University of Florida), P. R. Kumar 
(University of Illinois), P. S. Krishnaprasad (University of Maryland), Greg McRae 
(MIT), Jerrold Marsden (Caltech), George Meyer (NASA), WiUiam Powers (Ford), 
and Pravin Varaiya (UC Berkeley). A writing subcommittee consisting of Karl 
Astrom, Stephen Boyd, Roger Brockett, John Doyle, Richard Murray and Gunter 
Stein helped coordinate the generation of the report. 

The Panel held a meeting on 16-17 July 2000 at the University of Maryland, 
College Park to discuss the state of the field and its future opportunities. The 
meeting was attended by members of the Panel and invited participants from the 
academia, industry, and government. Additional meetings and discussions were held 
over the next 15 months, including presentations at DARPA and AFOSR sponsored 
workshops, meetings with government program managers, and writing committee 
meetings. The results of these meetings, combined with discussions among Panel 
members and within the community at workshops and conferences, form the main 
basis for the findings and recommendations of this Panel. 

A web site has been established to provide a central repository for materials 
generated by the Panel: 

http: //www. cds . caltech. edu/~murray/cdspanel/ 

viii Preface 

Copies of this report, links to other sources of information, and presentation mate- 
rials from the Panel workshop and other meetings can be found there. 

Several similar reports and papers highlighting future directions in control 
came to the Panel's attention during the development of this report. Many mem- 
bers of the Panel and participants in the June 2000 workshop were involved in 
the generation of the 1988 Fleming report [15] and a 1987 IEEE Transactions on 
Automatic Control article [25], both of which provided a roadmap for many of the 
activities of the last decade and continue to be relevant. More recently, the Euro- 
pean Commission sponsored a workshop on future control systems [14] and several 
other more focused workshops have been held over the last several years [1, 2, 33, 34]. 
Several recent papers and reports highlighted successes of control [35] and new vis- 
tas in control [11, 23]. The Panel also made extensive use of a recent NSF/CSS 
report on future directions in control engineering education [1], which provided a 
partial basis for Chapter 4 of the present report. 

The bulk of this report was written before the tragic events of September 11, 
2001, but control will clearly play a major role in the world's effort to combat terror- 
ism. From new methods for command and control of unmanned vehicles, to robust 
networks linking businesses, transportation systems, and energy infrastructure, to 
improved techniques for sensing and detection of biological and chemical agents, the 
techniques and insights from control will enable new methods for protecting human 
life and safeguarding our society. 

The Panel would like to thank the control community for its support of this 
report and the many contributions, comments, and discussions that help form the 
context and content for the report. We are particularly indebted to Dr. Marc Q. 
Jacobs for his initiative in the formation of the Panel and for his support of the 
project through AFOSR. 

Richard M. Murray Pasadena, June 2002 

Chapter 1 

Executive Summary 

Rapid advances in computing, communications, and sensing technology offer un- 
precedented opportunities for the field of control to expand its contributions to the 
economic and defense needs of the nation. This report presents the findings and 
recommendations of a panel of experts chartered to examine these opportunities. 
We present an overview of the field, review its successes and impact, and describe 
the new challenges ahead. We do not attempt to cover the entire field. Rather, we 
focus on those areas that are undergoing the most rapid change and that require 
new approaches to meet the challenges and opportunities that face the community. 

Overview of Control 

Control as defined in this report refers to the use of algorithms and feedback in 
engineered systems. At its simplest, a control system is a device in which a sensed 
quantity is used to modify the behavior of a system through computation and 
actuation. Control systems engineering traces its roots to the industrial revolution, 
to devices such as the centrifugal governor, shown in Figure 1.1. This device used 
a flyball mechanism to sense the rotational speed of a steam turbine and adjust 
the flow of steam into the machine using a series of linkages. By thus regulating 
the turbine's speed, it provided the safe , reliable , consistent operation that was 
required to enable the rapid spread of steam-powered factories. 

Control played an essential part in the development of technologies such as 
power, communications, transportation, and manufacturing. Examples include au- 
topilots in military and commercial aircraft (Figure 1.2a), regulation and control of 
the electrical power grid, and high accuracy positioning of read/write heads in disk 
drives (Figure 1.2b). Feedback is an enabling technology in a variety of application 
areas and has been reinvented and patented many times in different contexts. 

A modern view of control sees feedback as a tool for uncertainty management. 
By measuring the operation of a system, comparing it to a reference, and adjusting 
available control variables, we can cause the system to respond properly even if its 
dynamic behavior is not exactly known or if external disturbances tend to cause it 

Chapter 1. Executive Summary 


Figure 1.1. The centrifugal governor (a), developed in the 1780s, was an 
enahler of the successful Watt steam, engine (h), which fueled the industrial revolu- 
tion. Figures courtesy of Cambridge University. 

to respond incorrectly. This is an essential feature in engineering systems since they 
must operate reliably and efficiently under a variety of conditions. It is precisely 
this aspect of control as a means of ensuring robustness to uncertainty that ex- 
plains why feedback control systems are all around us in the modern technological 
world. They are in our homes, cars and consumer electronics, in our factories and 
communications systems, and in our transportation, military and space systems. 

The use of control is extremely broad and encompasses a number of different 
applications. These include control of electromechanical systems, where computer- 
controlled actuators and sensors regulate the behavior of the system; control of elec- 
tronic systems, where feedback is used to compensate for component or parameter 
variations and provide reliable, repeatable performance; and control of information 
and decision systems, where limited resources are dynamically allocated based on 
estimates of future needs. Control principles can also be found in areas such as 
biology, medicine, and economics, where feedback mechanisms are ever present. In- 
creasingly, control is also a mission critical function in engineering systems: the 
systems will fail if the control system does not work. 

Contributions to the field of control come from many disciplines, including 
pure and applied mathematics; aerospace, chemical, mechanical, and electrical en- 
gineering; operations research and economics; and the physical and biological sci- 
ences. The interaction with these different fields is an important part of the history 
and strength of the field. 

Successes and Impact 

Over the past 40 years, the advent of analog and digital electronics has allowed 
control technology to spread far beyond its initial applications, and has made it an 
enabling technology in many applications. Visible successes from past investment 

Figure 1.2. Applications of control: (a) the Boeing 777 fly-by-wire aircraft 
and (b) the Seagate Barracuda 36ES2 disk drive. Photographs courtesy of the Boeing 
Company and Seagate Technology. 

in control include: 

• Guidance and control systems for aerospace vehicles, including commercial 
aircraft, guided missiles, advanced fighter aircraft, launch vehicles, and satel- 
lites. These control systems provide stability and tracking in the presence of 
large environmental and system uncertainties. 

• Control systems in the manufacturing industries, from automotive to inte- 
grated circuits. Computer controlled machines provide the precise positioning 
and assembly required for high quality, high yield fabrication of components 
and products. 

• Industrial process control systems, particularly in the hydrocarbon and chemi- 
cal processing industries. These maintain high product quality by monitoring 
thousands of sensor signals and making corresponding adjustments to hun- 
dreds of valves, heaters, pumps, and other actuators. 

• Control of communications systems, including the telephone system, cellular 
phones, and the Internet. Control systems regulate the signal power lev- 
els in transmitters and repeaters, manage packet buffers in network routing 
equipment, and provide adaptive noise cancellation to respond to varying 
transmission line characteristics. 

These applications have had an enormous impact on the productivity of modern 

In addition to its impact on engineering applications, control has also made 
significant intellectual contributions. Control theorists and engineers have made 
rigorous use of and contributions to mathematics, motivated by the need to develop 
provably correct techniques for design of feedback systems. They have been consis- 
tent advocates of the "systems perspective," and have developed reliable techniques 

Chapter 1. Executive Summary 

Figure 1.3. Modern networked systems: (a) the California power grid and 
(h) the NSFNET Internet backbone. Figures courtesy of the state of California and 
the National Center for Supercomputer Applications (NCSA). 

for modeling, analysis, design, and testing that enable design and implementation 
of the wide variety of very complex engineering systems in use today. Moreover, 
the control community has been a major source and training ground for people who 
embrace this systems perspective and who wish to master the substantial set of 
knowledge and skills it entails. 

Future Opportunities and Challenges 

As we look forward, the opportunities for new applications that will build on ad- 
vances in control expand dramatically. The advent of ubiquitous, distributed com- 
putation, communication, and sensing systems has begun to create an environment 
in which we have access to enormous amounts of data and the ability to process 
and communicate that data in ways that were unimagined 20 years ago. This will 
have a profound effect on military, commercial and scientific applications, especially 
as software systems begin to interact with physical systems in more and more in- 
tegrated ways. Figure 1.3 illustrates two systems where these trends are already 
evident. Control will be an increasingly essential element of building such intercon- 
nected systems, providing high performance, high confidence, and reconfigurable 
operation in the presence of uncertainties. 

In all of these areas, a common feature is that system level requirements far 
exceed the achievable reliability of individual components. This is precisely where 
control (in its most general sense) plays a central role, since it allows the system 
to ensure that it is achieving its goal through correction of its actions based on 
sensing its current state. The challenge to the field is to go from the traditional 
view of control systems as a single process with a single controller, to recognizing 
control systems as a heterogeneous collection of physical and information systems. 

with intricate interconnections and interactions. 

In addition to inexpensive and pervasive computation, communication, and 
sensing — and the corresponding increased role of information-based systems — an 
important trend in control is the move from low-level control to higher levels of de- 
cision making. This includes such advances as increased autonomy in flight systems 
(all the way to complete unmanned operation), and integration of local feedback 
loops into enterprise-wide scheduling and resource allocation systems. Extending 
the benefits of control to these non-traditional systems offers enormous opportuni- 
ties in improved efficiency, productivity, safety, and reliability. 

Control is a critical technology in defense systems and is increasingly impor- 
tant in the fight against terrorism and asymmetric threats. Control allows the 
operation of autonomous and semi-autonomous unmanned systems for difficult and 
dangerous missions, as well as sophisticated command and control systems that 
enable robust, reconfigurable decision making systems. The use of control in mi- 
crosystems and senosr webs will improve our ability to detect threats before they 
cause damage. And new uses of feedback in communications systems will provide 
reliable, flexible, and secure networks for operation in dynamic, uncertain, and ad- 
versarial environments. 

In order to realize the potential of control applied to these emerging appli- 
cations, new methods and approaches must be developed. Among the challenges 
currently facing the field, a few examples provide insight into the difficulties ahead: 

• Control of systems with both symbolic and continuous dynamics. Next gener- 
ation systems will combine logical operations (such as symbolic reasoning and 
decision making) with continuous quantities (such as voltages, positions, and 
concentrations). The current theory is not well-tuned for dealing with such 
systems, especially as we scale to very large systems. 

• Control in distributed, asynchronous, networked environments. Control dis- 
tributed across multiple computational units, interconnected through packet- 
based communications, will require new formalisms for ensuring stability, per- 
formance and robustness. This is especially true in applications where one 
cannot ignore computational and communications constraints in performing 
control operations. 

• High level coordination and autonomy. Increasingly, feedback is being de- 
signed into enterprise-wide decision systems, including supply chain manage- 
ment and logistics, airspace management and air traffic control, and C4ISR 
systems. The advances of the last few decades in analysis and design of ro- 
bust control systems must be extended to these higher level decision making 
systems if they are to perform reliably in realistic settings. 

• Automatic synthesis of control algorithms, with integrated verification and val- 
idation. Future engineering systems will require the ability to rapidly de- 
sign, redesign and implement control software. Researchers need to develop 
much more powerful design tools that automate the entire control design pro- 
cess from model development to hardware-in-the-loop simulation, including 
system- level software verification and validation. 

Chapter 1. Executive Summary 

• Building very reliable systems from unreliable parts. Most large engineering 
systems must continue to operate even when individual components fail. In- 
creasingly, this requires designs that allow the system to automatically recon- 
figure itself so that its performance degrades gradually rather than abruptly. 

Each of these challenges will require many years of effort by the research community 
to make the results rigorous, practical, and widely available. They will also require 
investments by funding agencies to ensure that current progress is continued and 
that forthcoming technologies are realized to their fullest. 


To address these challenges and deliver on the promise of the control field, the Panel 
recommends that the following actions be undertaken: 

1. Substantially increase research aimed at the integration of control, computer 
science, communications, and networking. This includes principles, methods 
and tools for modeling and control of high level, networked, distributed sys- 
tems, and rigorous techniques for reliable, embedded, real-time software. 

2. Substantially increase research in control at higher levels of decision making, 
moving toward enterprise level systems. This includes work in dynamic re- 
source allocation in the presence of uncertainty, learning and adaptation, and 
artificial intelligence for dynamic systems. 

3. Explore high-risk, long-range applications of control to new domains such 
as nanotechnology, quantum mechanics, electromagnetics, biology, and envi- 
ronmental science. Dual investigator, interdisciplinary funding might be a 
particularly useful mechanism in this context. 

4. Maintain support for theory and interaction with mathematics, broadly in- 
terpreted. The strength of the field relies on its close contact with rigorous 
mathematics, and this will be increasingly important in the future. 

5. Invest in new approaches to education and outreach for the dissemination of 
control concepts and tools to non-traditional audiences. The community must 
do a better job of educating a broader range of scientists and engineers on the 
principles of feedback and the use of control to alter the dynamics of systems 
and manage uncertainty. 

The impact of control is one which will come through many applications, in 
aerospace and transportation, information and networking, robotics and intelligent 
machines, materials and processing, and biology and medicine. It will enable us to 
build more complex systems and to ensure that the systems we build are reliable, 
efficient, and robust. The Panel's recommendations are founded on the diverse 
heritage of rigorous work in control and are key actions to realize the opportunities 
of control in an information rich world. 

Chapter 2 

Overview of the Field 

Control is a field with broad relevance to a number of engineering applications. 
Its impact on modern society is both profound and often poorly understood. In 
this chapter, we provide an overview of the field, illustrated with examples and 
vignettes, and describe the new environment for control. 

2.1 What is Control? 

The term "control" has many meanings and often varies between communities. In 
this report, we define control to be the use of algorithms and feedback in engineered 
systems. Thus, control includes such examples as feedback loops in electronic am- 
plifiers, set point controllers in chemical and materials processing, "fly-by-wire" 
systems on aircraft, and even router protocols that control traffic flow on the Inter- 
net. Emerging applications include high confldence software systems, autonomous 
vehicles and robots, battlefleld management systems, and biologically engineered 
systems. At its core, control is an information science, and includes the use of 
information in both analog and digital representations. 

A modern controller senses the operation of a system, compares that against 
the desired behavior, computes corrective actions based on a model of the system's 
response to external inputs, and actuates the system to effect the desired change. 
This basic feedback loop of sensing, computation, and actuation is the central con- 
cept in control. The key issues in designing control logic are ensuring that the 
dynamics of the closed loop system are stable (bounded disturbances give bounded 
errors) and that dynamics have the desired behavior (good disturbance rejection, 
fast responsiveness to changes in operating point, etc). These properties are estab- 
lished using a variety of modeling and analysis techniques that capture the essential 
physics of the system and permit the exploration of possible behaviors in the pres- 
ence of uncertainty, noise, and component failures. 

A typical example of a modern control system is shown in Figure 2.1. The 
basic elements of of sensing, computation, and actuation are clearly seen. In mod- 
ern control systems, computation is typically implemented on a digital computer. 

Chapter 2. Overview of the Field 

external disturbances 






operator input 
Figure 2.1. Components of a modern control system. 

requiring the use of analog-to-digital (A/D) and digital-to-analog (D/A) converters. 
Uncertainty enters the system through noise in sensing and actuation subsystems, 
external disturbances that affect the underlying system physics, and uncertain dy- 
namics in the physical system (parameter errors, unmodeled effects, etc). 

The basic feedback loop of control is often combined with feedforward control, 
where a commanded actuator input is computed to achieve a desired action based 
on a model of the system. While feedback operates in a closed loop, with actions 
based on the deviation between measured and desired performance, feedforward 
operates in open loop, with actions taken based on plans. It is often advantageous 
to use feedback with feedforward to achieve both high performance and robustness. 

It is important to note that while feedback is a central element of control, feed- 
back as a phenomenon is ubiquitous in science and nature. Homeostasis in biological 
systems maintains thermal, chemical, and biological conditions through feedback. 
Global climate dynamics depend on the feedback interactions between the atmo- 
sphere, oceans, land, and the sun. Ecologies are filled with examples of feedback, 
resulting in complex interactions between animal and plant life. The dynamics of 
economies are based on the feedback between individuals and corporations through 
markets and the exchange of goods and services. 

While ideas and tools from control can be applied to these systems, we focus 
our attention in this report on the application of feedback to engineering systems. 
We also limit ourselves to a small subset of the many aspects of control, choosing 
to focus on those that are undergoing the most change and are most in need of new 
ideas and techniques. 

Control Theory 

Control theory refers to the mathematical framework used to analyze and synthesize 
control systems. Over the last 50 years, there has been careful attention by control 

2.1. What is Control? 

theorists to the issues of completeness and correctness. This includes substantial 
efforts by mathematicians and engineers to develop a solid foundation for proving 
stability and robustness of feedback controlled systems, and the development of 
computational tools that provide guaranteed performance in the presence of un- 
certainty. This rigor in approach is a hallmark of modern control and is largely 
responsible for the success it has enjoyed across a variety of disciplines. 

It is useful in this context to provide a brief history of the development of 
modern control theory. 

Automatic control traces its roots to the beginning of the industrial revolution, 
when simple governors were used to automatically maintain steam engine speed de- 
spite changes in loads, steam supply, and equipment. In the early 20th Century, 
the same principles were applied in the emerging field of electronics, yielding feed- 
back amplifiers that automatically maintained constant performance despite large 
variations in vacuum tube devices. 

The foundations of the theory of control are rooted in the 1940s, with the 
development of methods for single- input, single-output feedback loops, including 
transfer functions and Bode plots for modeling and analyzing frequency response 
and stability, and Nyquist plots and gain/phase margin for studying stability of 
feedback systems [9]. By designing feedback loops to avoid positive reinforcement 
of disturbances around a closed loop system, one can ensure that the system is 
stable and disturbances are attenuated. This first generation of techniques is known 
collectively as "classical control" and is still the standard introduction to controls 
for engineering students. 

In the 1960s, the second generation of control theory, known as "modern 
control," was developed to provide methods for multi- variable systems where many 
strongly coupled loops must be designed simultaneously. These tools made use of 
state space representations of control systems and were coupled with advances in 
numerical optimization and optimal control. These early state space methods made 
use of linear ordinary differential equations to study the response of systems, and 
control was achieved by placing the eigenvalues of the closed loop system to ensure 

At around this same time, optimal control theory also made great advances, 
with the establishment of the maximum principle of Pontryagin and the dynamic 
programming results of Bellman. Optimal control theory gave precise conditions 
under which a controller minimized a given cost function, either as an open loop 
input (such as computing the thrust for optimal trajectory generation) or as a 
closed loop feedback law. Estimation theory also benefited from results in optimal 
control, and the Kalman filter was developed and quickly became a standard tool 
used in many fields to estimate the internal states of a system given a (small) set 
of measured signals. 

Finally, in the 1980s the third generation of control theory, known as "robust 
multi- variable control," added powerful formal methods to guarantee desired closed 
loop properties in the face of uncertainties. In many ways, robust control brought 
back some of the key ideas from the early theory of control, where uncertainty was a 
dominant factor in the design methodology. Techniques from operator theory were 
extremely useful here and there was stronger interaction with mathematics, both 

10 Chapter 2. Overview of the Field 

in terms of using existing techniques and developing new mathematics. 

Over the past two decades, many other branches of control have appeared, 
including adaptive, nonlinear, geometric, hybrid, fuzzy, and neural control frame- 
works. All of these have built on the tradition of linking applications, theory, and 
computation to develop practical techniques with rigorous mathematics. Control 
also built on other disciplines, especially applied mathematics, physics, and opera- 
tions research. 

Today, control theory provides a rich methodology and a supporting set of 
mathematical principles and tools for analysis and design of feedback systems. It 
links four important concepts that are central to both engineered and natural sys- 
tems: dynamics, modeling, interconnection, and uncertainty. 

The role of dynamics is central to all control systems and control theory has 
developed a strong set of tools for analyzing stability and performance of dynamical 
systems. Through feedback, we can alter the behavior of a system to meet the needs 
of an application: systems that are unstable can be stabilized, systems that are 
sluggish can be made responsive, and systems that have drifting operating points 
can be held constant. Control theory provides a rich collection of techniques to 
analyze the stability and dynamic response of complex systems and to place bounds 
on the behavior of such systems by analyzing the gains of linear and nonlinear 
operators that describe their components. These techniques are particularly useful 
in the presence of disturbances, parametric uncertainty, and unmodeled dynamics — 
concepts that are often not treated in detail in traditional dynamics and dynamical 
systems courses. 

Control theory also provides new techniques for (control-oriented) system 
modeling and identification. Since models play an essential role in analysis and 
design of feedback systems, sophisticated tools have been developed to build such 
models. These include input/output representations of systems (how disturbances 
propagate through the system) and data-driven system identification techniques. 
The use of "forced response" experiments to build models of systems is well de- 
veloped in the control field and these tools find application in many disciplines, 
independent of the use of feedback. A strong theory of modeling has also been 
developed, allowing rigorous definitions of model fidelity and comparisons to exper- 
imental data. 

A third key concept in control theory is the role of interconnection between 
subsystems. Input/output representations of systems allow one to build models 
of very complex systems by linking component behaviors. The dynamics of the 
resulting system is determined not only by the dynamics of the components, but 
by the interconnection structure between these components. The tools of control 
provide a methodology for studying the characteristics of these interconnections and 
when they lead to stability, robustness, and desired performance. 

Finally, one of the powerful features of modern control theory is that it pro- 
vides an explicit framework for representing uncertainty. Thus, we can describe a 
"set" of systems that represent the possible instantiations of a system or the pos- 
sible descriptions of the system as it changes over time. While this framework is 
important for all of engineering, the control community has developed one of the 
most powerful collection of tools for dealing with uncertainty. This was necessary 

2.1. What is Control? 


(a) Engine Control Electronics 

1980 1983 1986 1989 1992 1995 1998 

(b) Control Technology Trends 





Figure 2.2. Trends in control technology: (a) the number of sen- 
sors, actuators and control functions in engine controls [6] and (b) illustration of 
cost/performance trends for component technologies. 

because the use of feedback is not entirely benign. In fact, it can lead to catastrophic 
failure if the uncertainty is not properly managed (through positive feedback, for 
example) . 

Control Technology 

Control technology includes sensing, actuation and computation, used together to 
produce a working system. Figure 2.2a shows some of the trends in sensing, ac- 
tuation, and computation in automotive applications. As in many other 
application areas, the number of sensors, actuators, and microprocessors is increas- 
ing dramatically, as new features such as antilock brakes, adaptive cruise control, 
active restraint systems, and enhanced engine controls are brought to market. The 
cost/performance curves for these technologies, as illustrated in Figure 2.2b, is also 
insightful. The costs of electronics technologies, such as sensing, computation, and 
communications, is decreasing dramatically, enabling more information processing. 
Perhaps the most important is the role of communications, which is now inexpensive 
enough to offer many new possibilities. 

Control is also closely related to the integration of software into physical sys- 
tems. Virtually all modern control systems are implemented using digital comput- 
ers. Often they are just a small part of much larger computing systems performing 
various other system management tasks. Because of this, control software becomes 
an integral part of the system design and is an enabler for many new features in 
products and processes. Online reconfiguration is a fundamental feature of com- 
puter controlled systems and this is, at its heart, a control issue. 

This trend toward increased use of software in systems is both an opportunity 
and a challenge for control. As embedded systems become ubiquitous and com- 
munication between these systems becomes commonplace, it is possible to design 
systems that are not only reconfigurable, but also aware of their condition and 
environment, and interactive with owners, users, and maintainers. These "smart 

12 Chapter 2. Overview of the Field 

systems" provide improved performance, reduced downtime, and new functionality 
that was unimaginable before the advent of inexpensive computation, communica- 
tions, and sensing. However, they also require increasingly sophisticated algorithms 
to guarantee performance in the face of uncertainty and component failures, and 
require new paradigms for verifying the software in a timely fashion. Our everyday 
experience with commercial word processors shows the difficulty involved in getting 
this right. 

One of the emerging areas in control technology is the generation of such 
real-time embedded software [32]. While often considered within the domain of 
computer science, the role of dynamics, modeling, interconnection, and uncertainty 
is increasingly making embedded systems synonymous with control systems. Thus 
control must embrace software as a key element of control technology and integrate 
computer science principles and paradigms into the discipline. This has already 
started in many areas, such as hybrid systems and robotics, where the continuous 
mathematics of dynamics and control are intersecting with the discrete mathematics 
of logic and computer science. 

Comparison with Other Disciplines 

Control engineering relies on and shares tools from physics (dynamics and mod- 
eling), computer science (information and software) and operations research (op- 
timization and game theory), but it is also different from these subjects, in both 
insights and approach. 

A key difference with many scientific disciplines is that control is fundamen- 
tally an engineering science. Unlike natural science, whose goal is to understand 
nature, the goal of engineering science is to understand and develop new systems 
that can benefit mankind. Typical examples are systems for transportation, elec- 
tricity, communication and entertainment that have contributed dramatically to the 
comfort of life. While engineering originally emerged as traditional disciplines such 
as mining, civil, mechanical, electrical and computing, control emerged as a systems 
discipline around 1950 and cut across these traditional disciplines. The pinnacle of 
achievement in engineering science is to find new systems principles that are essen- 
tial for dealing with complex man-made systems. Feedback is such a principle and 
it has had a profound impact on engineering systems. 

Perhaps the strongest area of overlap between control and other disciplines is 
in modeling of physical systems, which is common across all areas of engineering and 
science. One of the fundamental differences between control-oriented modeling and 
modeling in other disciplines is the way in which interactions between subsystems 
(components) are represented. Control relies on input/output modeling that allows 
many new insights into the behavior of systems, such as disturbance rejection and 
stable interconnection. Model reduction, where a simpler (lower-fidelity) descrip- 
tion of the dynamics is derived from a high fidelity model, is also very naturally 
described in an input/output framework. Perhaps most importantly, modeling in a 
control context allows the design of robust interconnections between subsystems, a 
feature that is crucial in the operation of all large, engineered systems. 

Control share many tools with the field of operations research. Optimization 

2.2. Control System Examples 13 

and differential games play central roles in each, and both solve problems of asset 
allocation in the face of uncertainty. The role of dynamics and interconnection 
(feedback) is much more ingrained within control, as well as the concepts of stability 
and dynamic performance. 

Control is also closely associated with computer science, since virtually all 
modern control algorithms are implemented in software. However, control algo- 
rithms and software are very different from traditional computer software. The 
physics (dynamics) of the system are paramount in analyzing and designing them 
and their (hard) real-time nature dominates issues of their implementation. From 
a software-centric perspective, an F-16 is simply another peripheral, while from a 
control-centric perspective, the computer is just another implementation medium 
for the feedback law. Neither of these are adequate abstractions, and this is one of 
the key areas identified in this report as both an opportunity and a need. 

2.2 Control System Examples 

Control systems are all around us in the modern technological world. They maintain 
the environment, lighting, and power in our buildings and factories, they regulate 
the operation of our cars, consumer electronics, and manufacturing processes, they 
enable our transportation and communications systems, and they are critical ele- 
ments in our military and space systems. For the most part, they are hidden from 
view, buried within the code of processors, executing their functions accurately 
and reliably. Nevertheless, their existence is a major intellectual and engineering 
accomplishment that is still evolving and growing, promising ever more important 
consequences to society. 

Early Examples 

The proliferation of control in engineered systems has occurred primarily in the 
latter half of the 20th Century. There are some familiar exceptions, such as the 
Watt governor described earlier and the thermostat (Figure 2.3a), designed at the 
turn of the century to regulate temperature of buildings. 

The thermostat, in particular, is often cited as a simple example of feedback 
control that everyone can understand. Namely, the device measures the tempera- 
ture in a building, compares that temperature to a desired set point, and uses the 
"feedback error" between these two to operate the heating plant, e.g., to turn heat- 
ing on when the temperature is too low and to turn if off when temperature is too 
high. This explanation captures the essence of feedback, but it is a bit too simple 
even for a basic device such as the thermostat. Actually, because lags and delays 
exist in the heating plant and sensor, a good thermostat does a bit of anticipation, 
turning the plant off before the error actually changes sign. This avoids excessive 
temperature swings and cycling of the heating plant. 

This modification illustrates that, even in simple cases, good control system 
design it not entirely trivial. It must take into account the dynamic behavior of 
the object being controlled in order to do a good job. The more complex the 
dynamic behavior, the more elaborate the modifications. In fact, the development of 


Chapter 2. Overview of the Field 









Figure 2.3. Early control devices: (a) Honeywell T86 thermostat, origi- 
nally introduced in 1953, (h) Chrysler cruise control system, introduced in the 1958 
Chrysler Imperial (note the centrifugal governor) [21]. 

a thorough theoretical understanding of the relationship between dynamic behavior 
and good controllers constitutes the most significant intellectual accomplishment 
of the control community, and the codification of this understanding into powerful 
computer aided engineering design tools makes all modern control systems possible. 

There are many other control system examples, of course, that have developed 
over the years with progressively increasing levels of sophistication and impact. An 
early system with broad public exposure was the "cruise control" option introduced 
on automobiles in 1958 (see Figure 2.3b). With cruise control, ordinary people 
experienced the dynamic behavior of closed loop feedback systems in action — the 
slowdown error as the system climbs a grade, the gradual reduction of that error 
due to integral action in the controller, the small (but unavoidable) overshoot at the 
top of the climb, etc. More importantly, by experiencing these systems operating 
reliably and robustly, the public learned to trust and accept feedback systems, 
permitting their increasing proliferation all around us. Later control systems on 
automobiles have had more concrete impact, such as emission controls and fuel 
metering systems that have achieved major reductions of pollutants and increases 
in fuel economy. 

In the industrial world, control systems have been key enabling technologies 
for everything from factory automation (starting with numerically controlled ma- 
chine tools), to process control in oil refineries and chemical plants, to integrated 
circuit manufacturing, to power generation and distribution. They now also play 
critical roles in the routing of messages across the Internet (TCP/IP) and in power 
management for wireless communication systems. 

Aerospace Applications 

Similarly, control systems have been critical enablers in the aerospace and military 
world. We are familiar, for example, with the saturation bombing campaigns of 

2.2. Control System Examples 


Figure 2.4. Flight systems: (a) 1903 Wright Flyer, (b) X- 29 forward swept 
wing aircraft, in 1987. X-29 photograph courtesy of NASA Dryden Flight Research 

World War II, which dropped unguided explosives almost indiscriminately on pop- 
ulation centers in order to destroy selected industrial or military targets. These 
have been replaced with precision guided weapons with uncanny accuracy, a single 
round for a single target. This is enabled by advanced control systems, combining 
inertial guidance sensors , radar and infrared homing seekers, satellite navigation 
updates from the global positioning system, and sophisticated processing of the 
"feedback error," all combined in an affordably disposable package. 

We are also familiar with early space launches. Slender rockets balanced pre- 
cariously on the launch pad, failing too often in out-of-control tumbles or fireballs 
shortly after ignition. Robust, reliable, and well-designed control systems are not 
optional here, because boosters themselves are unstable. And control systems have 
lived up to this challenge. We now take routine launch operations for granted, 
supporting manned space stations, probes to the outer planets, and a host of satel- 
lites for communications, navigation, surveillance, and earth observation missions. 
Of course, these payloads are themselves critically dependent on robust, reliable 
and well-designed control systems for everything from attitude control, to on-orbit 
station-keeping, thermal management, momentum management, communications, 

Flight Control 

Another notable success story for control in the aerospace world comes from the 
control of flight. This example illustrates just how significant the intellectual and 
technological accomplishments of control have been and how important their con- 
tinued evolution will be in the future. 

Control has played a key role in the development of aircraft from the very 
beginning. Indeed, the Wright brother's first powered flight was successful only 
because the aircraft included control surfaces (warpable wings and forward-mounted 
vertical and horizontal fins) that were adjusted continuously by the pilot to stabilize 

16 Chapter 2. Overview of the Field 

the flight [19] (see Figure 2.4a). These adjustments were critical because the Wright 
Flyer itself was unstable, and could not maintain steady flight on its own. 

Because pilot workload is high when flying unstable aircraft, most early air- 
craft that followed the Wright Flyer were designed to be statically stable. Still, as 
the size and performance capabilities of aircraft grew, their handling characteristics 
deteriorated. Designers then installed so-called "stability augmentation systems" — 
automatic control systems designed to modify dynamic behavior of aircraft slightly 
in order to make them easier to fly. These systems first appeared during the World 
War II years. They used early inertial sensors to measure flight motions, analog 
electronic systems to construct and process feedback errors, and hydraulic systems 
to actuate the linkages of selected control surfaces (vertical and horizontal tails, 
ailerons, etc). 

Two issues surfaced immediately as these systems were being fielded: (1) how 
to design the control logic systematically (early systems were essentially developed 
by trial-and-error), and (2) how to build the systems such that they would operate 
reliably. Early systems proved to be quite unreliable. Hence, only a small fraction 
of the full authority of the control surfaces was typically allocated to the automatic 
system, with the bulk of authority reserved for manual control, so the pilot could 
always override the automation. 

Control theorists provided the solution for the first issue. They developed 
modeling and simulation methods (based on differential equations and transfer func- 
tions) that accurately describe aircraft dynamics, and they developed increasingly 
powerful generations of control analysis and design methods to design control laws. 
Classical control methods enabled the systematic design of early stability augmen- 
tation systems, while modern control and robust multi-variable control are critical 
in all of today's modern fiight systems. 

But analysis and design methods alone could not address the second issue of 
early stability augmentation systems, namely the need for highly reliable control 
implementations. That issue was resolved with the development of airborne dig- 
ital computers and redundant architectures. These are now routinely used on all 
commercial and military aircraft. They have become so highly reliable that the old 
solution of granting only partial authority to automation has long been abandoned. 
In fact, most modern fiight control implementations do not even include mechan- 
ical linkages between pilots and control surfaces. All sensed signals and control 
commands go through the digital implementation (e.g., fiy-by-wire). 

Today, we even entrust the very survival of aircraft to automation. Examples 
include the all weather auto-land functions of commercial transports, in which safe 
go-around maneuvers are not available if failures were to occur at certain critical 
fiight phases. Other examples include the F-16, B-2, and X-29 military aircraft (see 
Figure 2.4), whose basic dynamics are unstable like the Wright Flyer, but so much 
more violently that manual stabilization is not possible. Finally, in modern fiight 
systems there is a growing trend to automate more and more functions — all the way 
to removing the pilot entirely from the cockpit. This is already commonplace in 
certain military reconnaissance and surveillance missions and will soon be extended 
to more lethal ones, such as suppressing enemy air defenses with unmanned aerial 
vehicles (UAVs). 

2.2. Control System Examples 17 

The following vignette describes some of these advances, from the perspective 
of one of its successful practitioners. 

Vignette: Fighter Aircraft and Missiles (Kevin A. Wise, The Boeing 

The 1990s has been a decade of significant accomplishments and change for the 
aerospace community. New systems such as unstable, tailless aircraft, propulsion con- 
trolled ejection seats, and low-cost, accurate, GPS guided munitions were developed. 
Fly-by-wire flight control systems have become the standard, making control system de- 
sign and analysis central to military aircraft and missile system development. Improving 
pilot safety and reducing costs were key focus areas in industry. 

Flight control system design methods using feedback linearization paved the way for 
new gain scheduled flight control systems for aircraft. This method, applied to the 
X-36 Tailless Agility Research aircraft and the F-15 ACTIVE, uniquely allows engineers 
to better design flying qualities into the aircraft, reducing design and development costs 
and improving pilot acceptance. Advances in robustness theory improved analysis tools 
allowing engineers to accurately predict and thus expand departure boundaries for these 
highly unstable aircraft. To further improve safety, these control laws were augmented 
with neural networks for reconfigurable and damage adaptive flight control. 

Missile systems, such as the Joint Direct Attack Munition (JDAM) and the Miniaturized 
Munition Technology Demonstrator (MMTD) developed their flight control designs us- 
ing state feedback optimal control, and then projecting out those states not measured 
by sensors . This method eliminated sensor hardware, reducing weight and costs, and 
proved to be completely automatable. The Fourth Generation Escape System (GEN4) 
ejection seat also used this approach for its control laws. In addition to needing optimal 
performance, advances in robustness theory were used to characterize the seat's con- 
trol system performance to uncertain crew member size and weight (95% male to 5% 
female). Autocode software tools for implementing controls systems also emerged in 
the 1990s. These computer aided design tools provide a single environment for control 
design and analysis as well as software design and test. They have greatly reduced the 
implementation and testing costs of flight control systems. 

The new challenge faced by the control community is the development of unmanned 
combat systems (munitions as well as aircraft) and concepts of operations for these 
systems to address the intelligent, increasingly hostile, rapidly changing environments 
faced by our war fighters. These systems must detect, identify, locate, prioritize, and 
employ ordinance to achieve permanent destruction of high value targets. New devel- 
opments in intelligent control, vision based control, mission planning, path planning, 
decision aiding, communication architectures, logistics and support concepts, and last 
but not least, software development, validation, and verification are needed to support 
these systems and make them affordable. 

18 Chapter 2. Overview of the Field 

2.3 The Increasing Role of Information- Based 

Early applications of control focused on the physics of the system being controlled, 
whether it was the thermal dynamics of buildings, the flight mechanics of an air- 
plane, or the tracking properties of a disk drive head. The situation we now face is 
one in which pervasive computing, sensing, and communications are common and 
the way that we interact with machines and they interact with each other is chang- 
ing rapidly. The consequences of this tremendous increase in information are also 
manifest in control, where we are now facing the challenges of controlling large- 
scale systems and networks that are well beyond the size and complexity of the 
traditional applications of control. 

One indication of this shift is the role that embedded systems and software play 
in modern technology, described briefly above. Modern computer control systems 
are capable of enormous amounts of decision making and control logic. Increasingly, 
these software systems are interacting with physical processes and introducing feed- 
back algorithms to improve performance and robustness. Already, the amount of 
logic-based code is overshadowing the traditional control algorithms in many appli- 
cations. Much of this logic is interwoven with the closed loop performance of the 
system, but systematic methods for analysis, veriflcation, and design have yet to be 

Another area where control of information-based systems will be increasingly 
important is in resource allocation systems. In this context, control can be described 
as the science and engineering of optimal dynamic resource allocation under uncer- 
tainty. We start with a mathematical model, of a system that describes how current 
actions or decisions can affect the future behavior of the system, including our un- 
certainty in that behavior. "Resource allocation" means that our decisions can be 
interpreted as managing a tradeoff between competing goals, or choosing from a 
limited set of possible actions. "Uncertainty" is critical: there is some possible vari- 
ation in the system's behavior, so that decisions have to be made taking different 
possibilities into account. Sources of uncertainty include incomplete or corrupted 
information available to the decision maker, uncertainty in the mathematical model 
used to model the system, and unpredictability of commands due to noise and 
disturbance signals that affect the system. While often considered an operations 
research problem, the role of dynamics and instabilities points to a clear need for 
control theory as well. 

One of the consequences of this shift toward information-based systems is that 
we are moving from an era where physics was the bottleneck to progress to one in 
which complexity is the bottleneck. 

There are already many examples of this new class of systems that are being 
deployed. Congestion control in routers for the Internet, power control in wireless 
communications systems, and real-time use of information in service and supply 
chains are a few examples. In all of these systems, it is the interaction of informa- 
tion flow with the underlying physics that is responsible for the overall performance. 
Another example is the air trafiic control network, where the density of flights, de- 
mand for efficiency, and intolerance for failure have created a situation that couples 

2.3. The Increasing Role of Information-Based Systems 


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San Francisco Bay Area 

Major Jel Arrival & Departure 

Routes -SE Plan 

>OAK ^^^ 
■ SFO ^^^ 
• SJO ^^^ 

Figure 2.5. 5'an Francisco Bay area aircraft arrival and departure routes. 
Figure courtesy of Federal Aviation Authority. 

vast amounts of information — everytiiing from ttie location of the planes to the indi- 
vidual customer itineraries — that must be managed to maintain high performance, 
robust, and reliable operation at all times. Figure 2.5 shows just one small part of 
this problem, the local departure and arrival routes in the San Francisco Bay area. 

There is an important role for control in many of these applications. As 
in traditional application areas, control serves as a mechanism for getting both 
information and, more importantly, action out of data. Furthermore, the theory 
of control provides insights and tools for analyzing and designing interconnected 
systems with desirable stability and robustness properties. 

One fundamental change in the use of control is the role of communications 
and networking. This will radically change the use of feedback in modern systems, 
through increased access to large amounts of information as well as the new envi- 
ronment in which control systems will have to operate. Control computations must 
increasingly be done in a distributed, partially asynchronous environment where 
tight control over the timing of data and computation is not available, due for 
example to the existence of packet-based communications networks between sens- 
ing, actuation, and computational nodes. Many traditional approaches may no 
longer work in this context and we anticipate the need to develop new paradigms 
for designing robust, high performance, feedback systems in this information rich 

The role of uncertainty in information rich systems is also critical (and largely 

20 Chapter 2. Overview of the Field 

unexplored) and concepts from control will play an important role in managing 
this uncertainty in the analysis, design, and operation of large-scale, interconnected 
systems. Uncertainty must be represented in order to build tractable models for 
answering questions that take into account the whole range of possible variations 
in the details of components and their interconnections. Control ideas will be in- 
creasingly important as a tool for managing both the complexity and uncertainty in 
these systems, and must be made available to the designers of such systems, through 
education and design software. One aspect of this that is likely to be particularly 
important is the exploration of fundamental limits of performance, robustness, and 
stability, since tradeoffs between these will be the primary design challenge in this 

Examples of the need for increased development in this area can be seen in the 
applications discussed in the next chapter. Vehicle, mission, and airspace manage- 
ment systems for transportation; source, power, and router control for networks; 
and genetic, cellular, and integrative feedback networks in biological systems are 
just a few examples. The simplest of these problems lies at the boundaries of 
current tools and understanding, and future progress will require a much deeper 
understanding of the integration between control, communications, computing, and 
networks as well as modeling, analysis, and control of complex decision systems. 

2.4 Opportunities and Challenges Facing the Field 

Control has developed into a major field in which generations of engineers are able 
to solve problems of practical importance and enormous impact. Over the past few 
years, the opportunities for control have expanded enormously, but there are many 
challenges that must be addressed to realize the potential for impact. In this section 
we attempt to characterize some of the overarching themes that describe these 
opportunities and challenges, and recommend an approach for moving forward. 

Characteristics of the New Environment 

The future of control will be driven by a new environment that differs substantially 
from that of the past 40 years. Some of the features of this new environment are 
already apparent and provide insight into the new research directions that must be 

Ubiquitous Computation, Communication and Sensing. The dominant change in 
the engineering environment is the presence of ever more powerful computation 
and cheaper communication. The new software and storage products that these 
developments have spawned have further changed the engineering landscape in many 
areas. In addition, microelectronics and MEMS have made available inexpensive 
sensors , such as those shown in Figure 2.6, and new actuator concepts that can be 
made available via communication networks, allowing increasingly sensor rich and 
actuator rich control. 

It will require decades to take full advantage of these developments. Some 
innovation will involve standalone improvements to individual systems and some 

2.4. Opportunities and Challenges Facing the Field 


Figure 2.6. Examples of current sensor technology: (a) 1024x1024 CCD 
array, (h) MEMS-based microgryoscope, and (c) sensor web pod. All photographs 
courtesy of Jet Propulsion Laboratory. 

will involve extreme interconnectedness of the type seen in the telephone system, 
the power grid, the Internet, and their descendants. Both types may, and probably 
will, depend on the use of control. The new ideas required to be successful in 
the two cases are, however, likely to be qualitatively different because we do not 
yet have a great deal of experience in building and operating safe, reliable, highly 
interconnected systems. 

New Application Domains. In addition to the revolutionary changes in information 
technology, future control systems will involve interactions between physical, chem- 
ical, biological, and information sciences, integrated with algorithms and feedback. 
This will open up new application domains for control, such as biological engineer- 
ing and quantum systems. While there are already researchers within the control 
community that are attacking problems in these areas, it will be necessary to ed- 
ucate new generations of researchers in both control and other disciplines in order 
to make advances in these applications. The possibilities for control are potentially 
very fundamental in nature, as illustrated in the following vignette. 

Vignette: Quantum Measurement and Control (Hideo Mabuchi, Cal- 

To illustrate the applications of control in new domains, consider the research of Hideo 
Mabuchi, who is exploring the use of feedback and control in quantum systems and its 
implications for unifying quantum and classical physics; 

A grand enigma, which is perhaps our primary legacy from 20th Century 
physics, is that the states and dynamics we ascribe to microscopic (quan- 
tum) systems seem incompatible with macroscopic (classical) phenomenol- 
ogy. For example, physical theory claims that it should be illogical simulta- 
neously to assign definite values to certain sets of measurable properties of 
a quantum system. And yet we want to believe that quantum mechanics 
is a correct description of microscopic physics, which evolves robustly into 
classical dynamics for systems of sufficiently large size and with a sufficiently 
high degree of interconnection among their manifold degrees of freedom. 

22 Chapter 2. Overview of the Field 

How can we understand the consistency of quantum mechanics, as a mi- 
croscopic theory, with classical physics as a manifestly valid description of 
macroscopic phenomena? 

Control theory provides a new set of tools for understanding quantum systems. One set 
of tools is through systematic techniques for model reduction: 

Viewed from a "multiscale" perspective, our challenge in explaining the 
quantum-classical transition will be to show that classical physics can rig- 
orously be obtained as a robust and parsimonious approximation to the 
dynamics of certain aggregate degrees of freedom for generic complex quan- 
tum systems. In the language of control theory, one would like to derive 
classical physics as an optimal model reduction of quantum physics. A 
number of fundamental questions arise as soon as the problem is posed this 
way. How can this model reduction be so general and robust, depending 
only upon the structure of quantum theory and not the details of any par- 
ticular dynamical system? What are the general parameters that control 
the error bounds on this model reduction? What impact will this program 
have, if successful, on our basic interpretation of quantum mechanics? 

In addition, control can provide new techniques for doing experiments, allowing us to 
better explore physical understanding: 

we hope that feedback control will provide a crucial experimental 
methodology for scrutinizing the validity of quantum measurement the- 
ory in realistic laboratory scenarios, especially with regard to the equations 
for conditional evolution of a system under continuous observation. Such 
equations could be used as the starting point for controller synthesis, for ex- 
ample, and their validity would be assessed by comparison of experimentally 
observed closed-loop behavior with theoretical expectations. 

Mabuchi's work illustrates the potential power of control theory as a disruptive tech- 
nology for understanding the world around us. 

Reliable Systems with Unreliable Parts. Most reasonably complex man-niade sys- 
tems are not rendered inoperable by the failure of any particular component and 
biological systems often demonstrate remarkable robustness in this regard. Simple 
redundancy, or the spare parts approach to such problems, is of limited effectiveness 
because it is uneconomical. Designs that allow the system to reconfigure itself when 
a component fails, even if this degrades the performance roughly in proportion to 
the magnitude of the failure, are usually preferred. Although computer memory 
chips and disk drive controllers often take advantage of strategies of this type, it is 
still true that the design of self healing systems is not well studied or analyzed. 

This issue takes on considerable significance when dealing with interconnected 
systems of the complexity of the Internet. In this case there are billions of compo- 

2.4. Opportunities and Challenges Facing the Field 23 

nents and yet the system is so essential that httle downtime can be tolerated. 

Complexity. Air traffic control systems, power grid control systems and other large- 
scale, interconnected systems are typical of a class of problems whose complexity 
is fixed not by the designer but rather by economic considerations and the natural 
scale of the problem. An acceptable solution in this context must be capable of 
dealing with the given complexity. In deciding if a system can be built or not, it is 
important to correctly gauge the feasibility because there is no value in a product 
that "almost" works. 

Every discipline has methods for dealing with some types of complexity. In 
the physical sciences, for example, the tools developed for studying statistical me- 
chanics have lead to a very substantial body of literature, effective for solving some 
problems. However, in discussing complexity it is one thing to find a point of view 
from which aspects of the behavior is compressible (e.g., "the entropy of a closed 
system can only increase" ) but it is another to have a "theory of complex systems" . 
The latter is something of an oxymoron, in that it suggests that the system is not 
really complex. On the other hand, it does make sense to seek to understand and 
organize the methodologies which have proven to be useful in the design of highly 
interconnected systems and to study naturally occurring systems with this in mind. 
Engineers looking at the immune system may very well be able to suggest new 
methods to defeat Internet viruses and ideas from neuroscience may inspire new 
developments in building reliable systems using unreliable components. 

Vision for the Future 

This new environment for control presents many challenges, but also many opportu- 
nities for impact across a broad variety of application areas. The future directions 
in control, dynamics, and systems must continue to address fundamental issues, 
guided by new applications. 

One of the biggest challenges facing the field is the integration of computa- 
tion, communications, and control. As computing, communications, and sensing 
become more ubiquitous, the use of control will become increasingly ubiquitous as 
well. However, many of the standard paradigms that allow the separation of these 
different disciplines will no longer be valid. For example, the ability to separate the 
computational architecture from the functions that are being computed is already 
beginning to unravel as we look at distributed systems with redundant, intermit- 
tent, and sometimes unreliable computational elements. Beyond simply looking at 
hybrid systems, a theory must be developed that integrates computer science and 

Similarly, the simplification that two nodes that are connected can communi- 
cate with sufficient reliability and bandwidth such that the properties of the com- 
munications channel can be ignored no longer holds in the highly networked envi- 
ronment of the future. Control must become more integrated with the protocols 
of communications so that high response feedback loops are able to use the same 
channels as high throughput, lower bandwidth information, without interfering with 
each other. 

24 Chapter 2. Overview of the Field 

Another element of the future of control is to begin to understand analysis 
and synthesis of control using higher levels of decision making. Traditionally control 
has dealt with the problem of keeping a few variables constant (regulation) or 
making variables follow specified time functions (tracking). In robotics, control 
was faced with more complicated problems such as obstacle avoidance and path 
planning (task-based control). Future systems will require that control be applied 
to problems that cannot necessarily be expressed in terms of continuous variables, 
but rather have symbolic, linguistic, or protocol-based descriptions. This is required 
as we move to more sophisticated autonomous and semi-autonomous systems that 
require high-level decision making capabilities. 

At the same time as control moves to higher levels of decision making, it will 
also move to new domains that are only beginning to emerge at the present time. 
This includes biological, quantum and environmental systems; software systems; 
enterprise level systems; and economic and financial systems. In all of these new 
problem domains, it will be necessary to develop a rigorous theory of control. This 
has been a historical strength of the field and has allowed it to be successful in an 
enormous number of systems. 

Finally, we envision an increased awareness of control principles in science and 
engineering, including much more exposure to feedback systems in math and science 


The opportunities and challenges describe here should be addressed on two fronts. 
There is a need for a broadly supported, active research program whose goals are to 
explore and further develop methodologies for design and operation of reliable and 
robust highly interactive systems, and there is a need to make room in the academic 
programs for material specific to this area. 

The research program must be better integrated with research activities in 
other disciplines and include scientists trained in software engineering, molecular 
biology, statistical mechanics, systems engineering and psychology. Control re- 
searchers must continue to branch out beyond traditional discipline boundaries and 
become participants and contributors in areas such as computer science, biology, 
economics, environmental science, materials science and operations research. There 
is particular need for increased control research in information-based systems, in- 
cluding communications, software, verification and validation, and logistics. 

To support this broader research program, a renewed academic program must 
also be developed. This program should strengthen the systems view and stretch 
across traditional discipline boundaries. To do so, it will be necessary to provide 
better dissemination of tools to new communities and provide a broader education 
for control engineers and researchers. This will require considerable effort to present 
current knowledge in a more compact way and to allow new results in software, 
communications, and emerging application domains to be added, while maintaining 
the key principles of control on which new results will rest. Simultaneously, the 
control community must seek to increase exposure to feedback in math and science 
education at all levels, even K-12. Feedback is a fundamental principle that should 

2.4. Opportunities and Challenges Facing the Field 25 

be part of every technically literate person's knowledge base. 

One of the characteristics of the control field has been an emphasis on theory 
and mathematical formulations of the problems being considered. This discipline 
has resulted in a body of work that is reliable and unambiguous. Moreover, because 
this style appeals to some very able graduate students, it has been an important 
factor in maintaining the flow of talent into the field. However, for engineers and 
scientists this has been a barrier to entry and can make it difficult for outsiders 
to assimilate and use the work in their own field. In addition, it has sometimes 
had a chilling effect on the development of ideas that are not easily translated into 
mathematical form. The challenge presented by the need to steer a course between 
the possible extremes here is not new, it has always been present. What is new 
is that the availability of easily used simulation tools has made the use of heuris- 
tic reasoning both more appealing and more reliable. In particular, optimization 
involving problems that are so large and/or so badly non-convex that rigorous anal- 
ysis is infeasible can now be approached using principled heuristics. Because of the 
software and computing power now available this may be the most effective way to 
proceed. It is important to find a place for effective heuristics in the training of 
students and the highest level professional meetings of the field. 

Finally, experimentation on representative systems must be an integral part 
of the control community's approach. The continued growth of experiments, both 
in education and research, should be supported and new experiments that refiect 
the new environment will need to be developed. These experiments are important 
for the insight into application domains that they bring, as well as the development 
of software and algorithms for applying new theory. But they also form the training 
ground for systems engineers, who learn about modeling, robustness, interconnec- 
tion, and data analysis through their experiences on real systems. 

The recommendations of the Panel, detailed in Chapter 5, provide a high 
level plan for implementing this basic approach. The recommendations focus on 
the need to pursue vigorously new application domains and, in particular, those 
domains in which the principles of control will be essential for future progress. 
They also highlight the need to maintain the field's strong theoretical base and 
historical rigor, while at the same time finding new ways to broaden the exposure 
and use of control to a broader collection of scientists and engineers. 

The new environment that control faces is one with many new challenges and 
an enormous array of opportunities. Advancing the state of the art will require that 
that the community accelerate its integration across disciplines and look beyond the 
current paradigms to tackle the next generation of applications. In the next chapter, 
we explore some of the application areas in more detail and identify some of the 
specific advancements that will be rec^uired. 


Chapter 2. Overview of the Field 

Chapter 3 

Opportunities, and 

In this chapter, we consider some of the opportunities and challenges for control 
in different application areas. The Panel decided to organize the treatment of 
applications around five main areas to identify the overarching themes that would 
guide its recommendations. These are: 

• Aerospace and transportation 

• Information and networks 

• Robotics and intelligent machines 

• Biology and medicine 

• Materials and processing 

In addition, several additional areas arose over the course of the Panel's delibera- 
tions, including environmental science and engineering, economics and finance, and 
molecular and quantum systems. Taken together, these represent an enormous col- 
lection of applications and demonstrate the breadth of applicability of ideas from 

The opportunities and challenges in each of these application areas form the 
basis for the major recommendations in this report. In each area, we have sought 
the advice and insights not only of control researchers, but also experts in the 
application domains who might not consider themselves to be control researchers. 
In this way, we hoped to identify the true challenges in each area, rather than 
simply identifying interesting control problems that may not have a substantial 
opportunity for impact. We hope that the findings will be of interest not only to 
control researchers, but also to scientists and engineers seeking to understand how 
control tools might be applied to their discipline. 

There were several overarching themes that arose across all of the areas con- 
sidered by the Panel. The use of systematic and rigorous tools is considered critical 
to future success and is an important trademark of the field. At the same time, the 


28 Chapter 3. Applications, Opportunities, and Challenges 

next generation of problems will require a paradigm shift in control research and 
education. The increased information available across all application areas requires 
more integration with ideas from computer science and communications, as well as 
improved tools for modeling, analysis, and synthesis for complex decision systems 
that contain a mixture of symbolic and continuous dynamics. The need to continue 
research in the theoretical foundations that will underly future advances was also 
common across all of the applications. 

In each section that follows we give a brief description of the background and 
history of control in that domain, followed by a selected set of topics which are used 
to explore the future potential for control and the technical challenges that must be 
addressed. As in the rest of the report, we do not attempt to be comprehensive in 
our choice of topics, but rather highlight some of the areas where we see the greatest 
potential for impact. Throughout these sections, we have limited the references to 
those that provide historical context, future directions, or broad overviews in the 
topic area, rather than specific technical contributions (which are too numerous to 
properly document). 

3.1. Aerospace and Transportation 29 

3.1 Aerospace and Transportation 

Men already know how to construct wings or airplanes, which when driven through 
the air at sufficient speed, will not only sustain the weight of the wings themselves, 
but also that of the engine, and of the engineer as well. Men also know how to 
build engines and screws of sufficient lightness and power to drive these planes at 
sustaining speed . . . Inability to balance and steer still confronts students of the flying 
problem. . . . When this one feature has been worked out, the age of flying will have 
arrived, for all other difficulties are of minor importance. 

Wilbur Wright, lecturing to the Western Society of Engineers in 1901 [30]. 

Aerospace and transportation encompasses a collection of critically important 
application areas where control is a key enabling technology. These application areas 
represent a very large part of the modern world's overall technological capability. 
They are also a major part of its economic strength, and they contribute greatly to 
the well being of its people. The historical role of control in these application areas, 
the current challenges in these areas, and the projected future needs all strongly 
support the recommendations of this report. 

The Historical Role 

In aerospace, specifically, control has been a key technological capability tracing 
back to the very beginning of the 20th Century. Indeed, the Wright brothers are cor- 
rectly famous not simply for demonstrating powered flight — they actually demon- 
strated controlled powered flight. Their early Wright Flyer incorporated moving 
control surfaces (vertical fins and canards) and warpable wings that allowed the 
pilot to regulate the aircraft's flight. In fact, the aircraft itself was not stable, so 
continuous pilot corrections were mandatory. This early example of controlled flight 
is followed by a fascinating success story of continuous improvements in flight con- 
trol technology, culminating in the very high performance, highly reliable automatic 
flight control systems we see on modern commercial and military aircraft today (see 
Fighter Aircraft and Missiles Vignette, page 17). 

Similar success stories for control technology occurred in many other aerospace 
application areas. Early World War II bombsights and fire control servo systems 
have evolved into today's highly accurate radar guided guns and precision guided 
weapons. Early failure-prone space missions have evolved into routine launch oper- 
ations, manned landings on the moon, permanently manned space stations, robotic 
vehicles roving Mars, orbiting vehicles at the outer planets, and a host of commer- 
cial and military satellites serving various surveillance, communication, navigation 
and earth observation needs. 

Similarly, control technology has played a key role in the continuing improve- 
ment and evolution of transportation — in our cars, highways, trains, ships and air 
transportation systems. Control's contribution to the dramatic increases of safety, 
reliability and fuel economy of the automobile is particularly noteworthy. Cars 
have advanced from manually tuned mechanical/pneumatic technology to computer 
controlled operation of all major functions including fuel injection, emission con- 
trol, cruise control, braking, cabin comfort, etc. Indeed, modern automobiles carry 

30 Chapter 3. Applications, Opportunities, and Challenges 

dozens of individual processors to see to it that these functions are performed ac- 
curately and reliably over long periods of time and in very tough environments. A 
historical perspective of these advances in automotive applications is provided in 
the following vignette. 

Vignette: Emissions Requirements and Electronic Controls for Automo- 
tive Systems (Mark Barron and William Powers, Ford Motor Company) 

One of the major success stories for electronic controls is the development of sophis- 
ticated engine controls for reducing emissions and improving efficiency. Mark Barron 
and Bill Powers described some of these advances in an article written in 1996 for the 
inaugural issue of the lEEE/ASME Transactions on Mechatronics [6]. 

In their article, Barron and Powers describe the environment that led up to the intro- 
duction of electronic controls in automobile engines: 

Except for manufacturing technology, the automobile was relatively benign 
with respect to technology until the late 1960s. Then two crises hit the 
automotive industry. The first was the environmental crisis. The environ- 
mental problems led to regulations which required a reduction in automotive 
emissions by roughly an order of magnitude. The second crisis was the oil 
embargo in the early 1970s which created fuel shortages, and which lead to 
legislation in the U.S. requiring a doubling of fuel economy. ... 

Requirements for improved fuel efficiency and lower emissions demanded 
that new approaches for controlling the engine be investigated. While today 
we take for granted the capabilities which have been made possible by 
the microprocessor, one must remember that the microprocessor wasn't 
invented until the early 1970s. When the first prototype of a computerized 
engine control system was developed in 1970, it utilized a minicomputer 
that filled the trunk of a car. But then the microprocessor was invented in 
1971, and by 1975 engine control had been reduced to the size of a battery 
and by 1977 to the size of a cigar box. 

These advances in hardware allowed sophisticated control laws that could deal with the 
complexities of maintaining low emissions and high fuel economy: 

The introduction in the late 1970s of the platinum catalytic converter was 
instrumental in reducing emissions to meet regulations. The catalytic con- 
verter is an impressive passive device which operates very effectively under 
certain conditions. One of the duties of the engine control system is to 
maintain those conditions by patterning the exhaust gases such that there 
are neither too many hydrocarbons nor too much oxygen entering the cata- 
lyst. If the ratio of air to fuel entering the engine is kept within a very tight 
range (i.e., a few percent) the catalyst can be over 90% efficient in remov- 
ing hydrocarbons, carbon monoxide, and oxides of nitrogen. However, the 
catalyst isn't effective until it has reached a stable operating temperature 
greater than 600°F (315°C), and a rule of thumb is that 80% of emissions 

3.1. Aerospace and Transportation 31 

which are generated under federal test procedures occur during the first two 
minutes of operation while the catalyst is warming to its peak efficiency op- 
erating temperature. On the other hand if the catalyst is operated for an 
extended period of time much above 1000°F (540°C) it will be destroyed. 
Excess fuel can be used to cool the catalyst, but the penalty is that fuel 
economy gets penalized. So the mechatronic system must not only control 
air-fuel ratios so as to maintain the catalyst at its optimum operating point, 
it must control the engine exhaust so that there is rapid lightoff of the cat- 
alyst without overheating, while simultaneously maintaining maximum fuel 

The success of control in meeting these challenges is evident in the reduction of emissions 
that has been achieved over the last 30 years [37]: 

US, European and Japanese Emission Standard continue to require signif- 
icant reductions in vehicle emissions. Looking closely at US passenger car 
emission standards, the 2005 level of hydrocarbon (HC) emissions is less 
than 2% of the 1970 allowance. By 2005, carbon monoxide (CO) will be 
only 10% of the 1970 level, while the permitted level for oxides of nitrogen 
will be down to 7% of the 1970 level. 

Furthermore, the experience gained in engine control provided a path for using electronic 
controls in many other applications [6]: 

Once the industry developed confidence in on-board computer control, other 
applications rapidly followed. Antilock brake systems, computer controlled 
suspension, steering systems and air bag passive restraint systems are ex- 
amples. The customer can see or feel these systems, or at least discern 
that they are on the vehicle, whereas the engine control system is not an 
application which is easily discernible by the customer. Computers are now 
being embedded in every major function of the vehicle, and we are seeing 
combinations of two or more of these control systems to provide new func- 
tions. An example is the blending of the engine and antilock brake system 
to provide a traction control system, which controls performance of the 
vehicle during acceleration whereas antilock brakes control performance of 
the vehicle during deceleration. 

An important consequence of the use of control in automobiles was its suc- 
cess in demonstrating that control provided safe and reliable operation. The cruise 
control option introduced in the late 1950s was one of the first servo systems receiv- 
ing very broad public exposure. Our society's inherent trust in control technology 
traces back to the success of such early control systems. 

Certainly, each of these successes owes its debt to improvements in many 
technologies, e.g. propulsion, materials, electronics, computers, sensors, navigation 
instruments, etc. However, they also depend in no small part on the continuous 


Chapter 3. Applications, Opportunities, and Challenges 

Figure 3.1. (a) The F-18 aircraft, one of the first production military 
fighters to use "fly-by-wire" technology, and (h) the X-45 (UCAV) unmanned aerial 
vehicle. Photographs courtesy of NASA Dryden Flight Research Center. 

improvements ttiat tiave occurred over ttie century in the ttieory, analysis metliods 
and design tools of control. As an example, "old timers" in the flight control engi- 
neering community still tell the story that early control systems (circa World War 
II) were designed by manually tuning feedback gains in flight — in essence, trial- 
and-error design performed on the actual aircraft. Dynamic modeling methods for 
aircraft were in their infancy at that time, and formal frequency-domain design 
theories to stabilize and shape single-input single-output feedback loops were still 
only subjects of academic study. Their incorporation into engineering practice rev- 
olutionized the field, enabling successful feedback systems designed for ever more 
complex applications, consistently, with minimal trial-and-error, and with reason- 
able total engineering effort. 

Of course, the formal modeling, analysis and control system design meth- 
ods described above have advanced dramatically since mid-century. As a result 
of significant R&D activities over the last fifty years, the state of the art today 
allows controllers to be designed for much more than single- input single-output sys- 
tems. The theory and tools handle many inputs, many outputs, complex uncertain 
dynamic behavior, difficult disturbance environments, and ambitious performance 
goals. In modern aircraft and transportation vehicles, dozens of feedback loops are 
not uncommon, and in process control the number of loops reaches well into the 
hundreds. Our ability to design and operate such systems consistently, reliably, 
and cost effectively rests in large part on the investments and accomplishments of 
control over the latter half of the century. 

Current Challenges and Future Needs 

Still, the control needs of some engineered systems today and those of many in the 
future outstrip the power of current tools and theories. This is so because current 
tools and theories apply most directly to problems whose dynamic behaviors are 

3.1. Aerospace and Transportation 33 

smooth and continuous, governed by underlying laws of physics and represented 
mathematically by (usually large) systems of differential equations. Most of the 
generality and the rigorously provable features of existing methods can be traced 
to this nature of the underlying dynamics. 

Many new control design problems no longer satisfy these underlying char- 
acteristics, at least in part. Design problems have grown from so-called "inner 
loops" in a control hierarchy (e.g. regulating a specified flight parameter) to various 
"outer loop" functions which provide logical regulation of operating modes, vehicle 
configurations, payload configurations, health status, etc [3]. For aircraft, these 
functions are collectively called "vehicle management." They have historically been 
performed by pilots or other human operators and have thus fallen on the other 
side of the man-machine boundary between humans and automation. Today, that 
boundary is moving! 

There are compelling reasons for the boundary to move. They include eco- 
nomics (two, one or no crew members in the cockpit versus three), safety (no opera- 
tors exposed to dangerous or hostile environments), and performance (no operator- 
imposed maneuver limits). A current example of these factors in action is the 
growing trend in all branches of the military services to field unmanned vehicles. 
Certain benign uses of such vehicles are already commonplace (e.g. reconnaissance 
and surveillance), while other more lethal ones are in serious development (e.g. 
combat UAVs for suppression of enemy air defenses) [29]. Control design efforts 
for such applications must necessarily tackle the entire problem, including the tra- 
ditional inner loops, the vehicle management functions, and even the higher-level 
"mission management" functions coordinating groups of vehicles intent on satisfying 
specified mission objectives. 

Today's engineering methods for designing the upper layers of this hierarchy 
are far from formal and systematic. In essence, they consist of collecting long lists 
of logical if-then-else rules from experts, programming these rules, and simulating 
their execution in operating environments. Because the logical rules provide no 
inherent smoothness (any state transition is possible) only simulation can be used 
for evaluation and only exhaustive simulation can guarantee good design proper- 
ties. Clearly, this is an unacceptable circumstance — one where the strong system- 
theoretic background and the tradition of rigor held by the control community can 
make substantial contributions. 

One can speculate about the forms that improved theories and tools for non- 
smooth (hybrid) dynamical systems might take. For example, it may be possible to 
impose formal restrictions on permitted logical operations, to play a regularizing role 
comparable to laws of physics. If rigorously obeyed, these restrictions could make 
resulting systems amenable to formal analyses and proofs of desired properties. 
This approach is similar to computer language design, and provides support for 
one of the recommendations of this report, namely that the control and computer 
science disciplines need to grow their intimate interactions. It is also likely that the 
traditional standards of formal rigor must expand to firmly embrace computation, 
algorithmic solutions, and heuristics. 

However, one must not ever lose sight of the key distinguishing features of the 
control discipline, including the need for hard real time execution of control laws and 


Chapter 3. Applications, Opportunities, and Challenges 

Figure 3.2. Battle space management scenario illustrating distributed com- 
mand and control between heterogeneous air and ground assets. Figure courtesy of 

the need for ultra-reliable operation of all hardware and software control compo- 
nents. Many controlled systems today (auto-land systems of commercial transports, 
launch boosters, F-16 and B-2 aircraft, certain power plants, certain chemical pro- 
cess plants, etc.) fail catastrophically in the event of control hardware failures, and 
many future systems, including the unmanned vehicles mentioned above, share this 
property. But the future of aerospace and transportation holds still more complex 
challenges. We noted above that changes in the underlying dynamics of control 
design problems from continuous to hybrid are well under way. An even more dra- 
matic trend on the horizon is a change in dynamics to large collections of distributed 
entities with local computation, global communication connections, very little reg- 
ularity imposed by laws of physics, and no possibility to impose centralized control 
actions. Examples of this trend include the national airspace management problem, 
automated highway and traffic management, and command and control for future 
battlefields (Figure 3.2). 

The national airspace problem is particularly significant today, with eventual 
gridlock and congestion threatening the integrity of the existing air transportation 
system. Even with today's traffic, ground holds and airborne delays in ffights due 
to congestion in the skies have become so common that airlines automatically pad 
their ffight times with built-in delays. The structure of the air traffic control (ATC) 
system is partially blamed for these delays: the control is distributed from airspace 

3.1. Aerospace and Transportation 35 

region to airspace region, yet within a region the control is almost wholly centralized, 
with sensory information from aircraft sent to a human air traffic controller who 
uses ground-based navigation and surveillance equipment to manually route aircraft 
along sets of well-traveled routes. In today's system, bad weather, aircraft failure, 
and runway or airport closure have repercussions throughout the whole country. 
Efforts are now being made to improve the current system by developing cockpit 
"sensors " such as augmented GPS navigation systems and datalinks for aircraft 
to aircraft communication. Along with these new technologies, new hierarchical 
control methodologies are being proposed, which automate some of the functionality 
of ATC. This opens up a set of new challenges: the design of information-sharing 
mechanisms and new, distributed, verified embedded control schemes for separation 
assurance between aircraft, and the design of dynamic air traffic network topologies 
which aid in the safe routing of aircraft from origin to destination and which adapt 
to different traffic flows, are two areas which provide a tremendous opportunity to 
researchers in the control community. 

Finally, it is important to observe that the future also holds many applications 
that fall under the traditional control design paradigm, yet are worthy of research 
support because of their great impact. Conventional "inner loops" in automobiles, 
but for non-conventional power plants, are examples. Hybrid cars combining elec- 
trical drives with low-power internal combustion engines and fuel cell powered cars 
combining electrical drives with fuel cell generation both depend heavily of well- 
designed control systems to operate efficiently and reliably. Similarly, increased 
automation of traditional transportation systems such as ships and railroad cars, 
with added instrumentation and cargo-tracking systems will rely on advanced con- 
trol and schedule optimization to achieve maximum economic impact. Another 
conventional area is general aviation, where control systems to make small aircraft 
easy and safe to fly and increased automation to manage them are essential needs. 

Other Trends in Aerospace and Transportation 

In addition to the specific areas highlighted above, there are many other trends 
in aerospace and transportation that will benefit from and inform new results in 
control. We briefly describe a few of these here. 

Automotive Systems With 60 million vehicles produced each year, automotive 
systems are a major application area for control. Emission control regulations 
passed in the 1970s created a need for more sophisticated engine control systems that 
could provide clean and efficient operation in a variety of operating environments 
and over the lifetime of the car. The development of the microprocessor at that same 
time allowed the implementation of sophisticated algorithms that have reduced the 
emissions in automobiles by as much as a factor of 50 from their 1970 levels. 

Future automobile designs will rely even more heavily on electronic con- 
trols [37]. Figure 3.3 shows some of the components that are being considered 
for next generation vehicles. Many of these components will build on the use of 
control techniques, including radar-based speed and spacing control systems, chassis 
control technologies for stability enhancement and improved suspension characteris- 


Chapter 3. Applications, Opportunities, and Challenges 

Engine Spark 

Throttle Actuator 

Steering Actuator & Position 
and Effort Sensors 

Brake Actuator 
Wheel Speed Sensors 

Driver Controls & 

Supplemental Inflatable 

hiertial Sensors for 
Rotational/ Angular, Lateral. 

Longitudinal Acceleration 

GPS Antenna 

Digital Radii 
GPS Receiver 

Suspension Control for 
Damping and Height 

Video Camera 

Supplemental Inflatable 

Active Belt Pretensioners 

Transmission Gear 

Individual Wheel 
Brake Actuators 


Map Database 

Figure 3,3, Major future components for basic automotive vehicle functions [37]. 

tics, active control of suspension and braking, and active restraint systems for safety. 
In addition, more sophisticated use of networking and communications devices will 
allow enhanced energy management between components and vehicle diagnostics 
with owner/dealer notification. 

These new features will require highly integrated control systems that combine 
multiple components to provide overall stability and performance. Systems such as 
chassis control will require combining steering, braking, powertrain and suspension 
subsystems, along with adding new sensors. One can also imagine increased in- 
teraction between vehicles and the roadway infrastructure, as automated highways 
and self-controlled vehicles move from the research lab into applications. These lat- 
ter applications are particularly challenging since they begin to link heterogeneous 
vehicles through communications systems that will experience varying bandwidths 
and latency (time delays) depending on the local environment. Providing safe, re- 
liable, and comfortable operation for such systems is a major challenge for control 
and one that will have application in a variety of consumer, industrial, and military 

3.1. Aerospace and Transportation 37 

Aircraft Propulsion Systems Much more effective use of information in propul- 
sion systems is possible as the price/performance ratio of computation and sensing 
continues to drop. Intelligent turbine engines will ultimately lower lifetime operat- 
ing and maintenance costs, similar to current and upcoming automotive systems. 
They will provide advanced health, performance, and life management by embed- 
ding models of their operation and optimizing based on condition and mission. They 
will be more flexible and more tolerant of component faults, and will integrate into 
the owners asset management system, lowering maintenance and fleet management 
costs by making engine condition information available to the owner on demand 
and ensuring predictable asset availability. 

Detection of damage (diagnostics) and prediction of the implications (prog- 
nostics) are the heart of an intelligent engine. Detailed modeling of the thermofluid, 
structural, and mechanical systems, as well as the operational environment, is 
needed for such assessments. To allow on-product use accounting for system in- 
teractions, physics-based models will be constructed using advanced techniques in 
reduced-order modeling . This approach significantly extends recent engine compo- 
nent modeling. 

Embedded models can also be used for online optimization and control in 
real time. The benefit is the ability to customize engine performance to changes 
in operating conditions and the engine's environment through updates in the cost 
function, onboard model, and constraint set. Many of the challenges of designing 
controllers that are robust to a large set of uncertainties can thus be embedded in 
the online optimization, and robustness through a compromise design is replaced 
by always-optimal performance. 

Flow Control Flow control involves the use of reactive devices for modifying fluid 
flow for the purposes of enhanced operability. Sample applications for flow control 
include increased lift and reduced drag on aircraft wings, engine nacelles, compressor 
fan blades, and helicopter rotor blades; higher performance diffusers in gas turbines, 
industrial heaters and chillers, and engine inlets; wake management for reduction of 
resonant stress and blade vortex interaction; and enhanced mixing for combustion 
and noise applications. A number of devices have been explored in the past several 
years for actuation of flow fields. These range from novel air injection mechanisms 
for control of rotating stall and separation, to synthetic jets developed for mixing 
enhancement and vectoring, to MEMS devices for modulating boundary layers and 
flow around stagnation points. In addition, new sensing technology, such as micro 
anemometers, is also becoming available. 

These changes in sensing and actuation technology are enabling new applica- 
tions of control to unstable shear layers and separated flow, thermoacoustic instabil- 
ities, and compression system instabilities such as rotating stall and surge (see [10] 
for a recent survey). An emerging area of interest in hypersonic flight systems, 
where flow control techniques could provide a larger toolbox for design of vehicles, 
including drag reduction, novel methods for producing control forces, and better 
understanding of the complex physical phenomena at these speeds. 

38 Chapter 3. Applications, Opportunities, and Challenges 

Space Systems^ The exploitation of space systems for civil, commercial, defense, 
scientific, or intelligence purposes gives rise to a unique set of challenges in the 
area of control. For example, most space missions cannot be adequately tested on 
the ground prior to flight, which has a direct impact on many dynamics and con- 
trol problems. A three-pronged approach is required to address these challenging 
space system problems: (1) detailed modeling, including improved means of char- 
acterizing, at a very small scale, the fundamental physics of the systems; (2) flight 
demonstrations to characterize the behavior of representative systems; and (3) de- 
sign of navigation and control approaches that maintain performance (disturbance 
rejection and tracking) even with uncertainties, failures, and changing dynamics. 

There are two significant areas that can revolutionize the achievable perfor- 
mance from future space missions: flexible structure analysis and control, and space 
vehicle formation flying. These both impact the allowable size of the effective aper- 
ture, which influences the "imaging" performance, whether it is optical imaging or 
the collection of signals from a wide range of wavelengths. There are fundamental 
limitations that prevent further developments with monolithic mirrors (with the 
possible exception of inflatable and foldable membranes, which introduce their own 
extreme challenges) and the various segmented approaches — deployed arrays, teth- 
ered or freeflyer formations — provide the only solution. However, these approaches 
introduce challenging problems in characterizing the realistic dynamics and devel- 
oping sensing and control schemes to maintain the necessary optical tolerances. 

A signiflcant amount of work has been performed in the area of flexible struc- 
ture dynamics and control under the auspices of the Strategic Defense Initiative 
Organization (SDIO) in the 1970s and 80s. However, at the performance levels 
required for future missions (nanometers), much research remains to develop mod- 
els at the micro-dynamics level and control techniques that can adapt to system 
changes at these small scales. 

Similar problems exist with formation control for proposed imaging interferom- 
etry missions. These will require the development of control algorithms, actuators, 
and computation and communications networks. Sensors will also have to be de- 
veloped to measure deflections on the scale of nanometers over distances hundreds 
of meters through kilometers. Likewise, actuation systems of various types must 
be developed that can control on the scale of nanometers to microns with very low 
noise levels and flne resolution. The biases and residuals generally accepted due to 
particular approximations in navigation and control algorithms will no longer be 
acceptable. Furthermore, the simulation techniques used for veriflcation must, in 
some cases, maintain precision through tens of orders of magnitude separation in 
key states and parameters, over both long and short time-scales, and with stochas- 
tic noise inputs. In summary, in order to enable the next generations of advanced 
space systems, the fleld must address the micro- and nanoscale problems in analysis, 
sensing, control, and simulation, for individual elements and integrated systems. 

^Thc Panel would like to thank Jonathan How and Jesse Leitner for their contributions to this 

3.2. Information and Networks 39 

3.2 Information and Networks 

A typical congested gateway looks like a fire hose connected to a soda straw through a 
small funnel (the output queue). If, on average, packets arrive faster than they can 
leave, the funnel will fill up and eventually overflow. RED [Random Early Detection] 
is [a] simple regulator that monitors the level in the funnel and uses it to match the 
input rate to the output (by dropping excess traffic). As long as its control law is 
monotone non- decreasing and covers the full range of to 100% drop rate, RED 
works for any link, any bandwidth, any type of traffic. 

Van Jacobson, North American Network Operators' Group meeting, 1998 [20]. 

The rapid growth of communications networks provides several major oppor- 
tunities and challenges for control. Although there is overlap, we can divide these 
roughly into two main areas: control of networks and control over networks. 

Control of Networks 

Control of networks is a large area, spanning many topics, a few of which are 
briefly described here. The basic problems in control of networks include controlling 
congestion across network links, routing the flow of packets through the network, 
caching and updating data at multiple locations, and managing power levels for 
wireless networks. 

Several features of these control problems make them very challenging. The 
dominant feature is the extremely large scale of the system; the Internet is probably 
the largest feedback control system man has ever built. Another is the decentralized 
nature of the control problem: local decisions must be made quickly, and based only 
on local information. Stability is complicated by the presence of varying time lags, 
as information about the network state can only be observed or relayed to controllers 
after a time delay, and the effect of a local control action can be felt throughout the 
network after substantial delay. Uncertainty and variation in the network, through 
network topology, transmission channel characteristics, traffic demand, available 
resources, etc., may change constantly and unpredictably. Another complicating 
issue is the diverse traffic characteristics, in terms of arrival statistics at both the 
packet and flow time scales, and different requirements for quality of service, in 
terms of delay, bandwidth, and loss probability, that the network must support. 

Resources that must be managed in this environment include computing, stor- 
age and transmission capacities at end hosts and routers. Performance of such sys- 
tems is judged in many ways: throughput, delay, loss rates, fairness, reliability, as 
well as the speed and quality with which the network adapts to changing traffic 
patterns, changing resource availability, and changing network congestion. 

To illustrate these characteristics, we briefly describe the control mechanisms 
that can be invoked in serving a flle request from a client: network caching, con- 
gestion control, routing and power control. Figure 3.4 shows a typical map for the 
networking infrastructure that is used to process such a request. 

The problem of optimal network caching is to copy documents (or services) 
that are likely to be accessed often, from many different locations, on multiple 
servers. When the document is requested, it is returned by the nearest server. 


Chapter 3. Applications, Opportunities, and Challenges 

UUNET's North America Internet tlietwork 

ti^M M 

Figure 3.4. UUNET network backbone for North America. Figure cour- 
tesy WorldCom. 

Here, proximity may be measured by geographical distance, liop count, network 
congestion, server load or a combination. The goal is to reduce delay, relieve server 
load, balance network traffic, and improve service reliability. If changes are made 
to the source document, those changes (at a minimum) must be transmitted to the 
servers, which consume network bandwidth. 

The control problem is to devise a decentralized scheme for how often to 
update, where to cache copies of documents, and to which server a client request is 
directed, based on estimation and prediction of access patterns, network congestion, 
and server load. Clearly, current decisions affect the future state, such as future 
traffic on links, future buffer levels, delay and congestion, and server load. Thus a 
web of caches is a decentralized feedback system that is spatially distributed and 
interconnected, where control decisions are made asynchronously based on local and 
delayed information. 

When a large file is requested, the server that is selected to return the file 
breaks it into a stream of packets and transports them to the client in a rate- 
adaptive manner. This process is governed by the Transport Control Protocol 
(TCP). The client acknowledges successful reception of each packet and the stream 
of acknowledgment carries congestion information to the server. Congestion control 
is a distributed algorithm to share network resources among competing servers. It 
consists of two components: a source algorithm that dynamically adjusts the server 
rate in response to congestion in its path, and a router algorithm that updates 
a congestion measure and sends it back to sources that go through that router. 
Examples of congestion measures are loss probability and queuing delay. They are 
implicitly updated at the routers and implicitly fed back to sources through delayed 

3.2. Information and Networks 41 

end-to-end observations of packet loss or delay. The equilibrium and dynamics of 
the network depends on the pair of source and router algorithms. 

A good way to understand the system behavior is to regard the source rates as 
primal variables and router congestion measures as dual variables, and the process of 
congestion control as an asynchronous distributed primal-dual algorithm carried out 
by sources and routers over the Internet in real time to maximize aggregate source 
utility subject to resource capacity constraints. Different protocols all solve the 
same prototypical problem, but they use different utility functions and implement 
different iterative rules to optimize them. Given any source algorithm, it is possible 
to derive explicitly the utility function it is implicitly optimizing. 

While TCP controls the rate of a packet flow, the path through the network 
is controlled by the Internet Protocol (IP). In its simplest form, each router must 
decide which output link a given packet will be sent to on its way to its final 
destination. Uncertainties include varying link congestion, delays, and rates, and 
even varying network topology (e.g., a link goes down, or new nodes or links become 
available), as well as future traffic levels. A routing algorithm is an asynchronous 
distributed algorithm executed at routers that adapts to node and link failures, 
balances network traffic and reduces congestion. It can be decomposed into several 
time scales, with very fast decisions made in hardware using lookup tables, which 
in turn are updated on a slower time scale. At the other extreme in time scale from 
the routing problem, we have optimal network planning, in which new links and 
nodes are proposed to meet predicted future traffic demand. 

The routing problem is further exacerbated in wireless networks. Nodes with 
wireless modems may be mobile, and the address of a node may neither indicate 
where it is located nor how to reach it. Thus the network needs to either search 
for a node on demand, or it must keep track of the changing locations of nodes. 
Further, since link capacities in wireless networks may be scarce, routing may have 
to be determined in conjunction with some form of load balancing. This gives rise 
to the need for distributed asynchronous algorithms which are adaptive to node 
locations, link failures, mobility, and changes in traffic flow requirements. 

Finally, if the client requesting the file accesses it through an ad hoc wireless 
network, then there also arises the problem of power control: at what transmis- 
sion power level should each packet broadcast be made? Power control is required 
because ad hoc networks do not come with ready made links; the topology of the 
network is formed by individual nodes choosing the power levels of their broadcasts. 
This poses a conceptual problem in the current protocol hierarchy of the Internet 
since it simultaneously affects the physical layer due to its effect on signal quality, 
the network layer since power levels determine which links are available for traffic 
to be routed, and the transport layer since power levels of broadcasts affect conges- 
tion. Power control is also a good challenge for multi-objective control since there 
are many cost criteria involved, such as increasing the traffic carrying capacity of 
the network, reducing the battery power used in relaying traffic, and reducing the 
contention for the common shared medium by the nodes in geographical vicinity. 

Control of networks extends beyond data and communication networks. Opti- 
mal routing and flow control of commercial aircraft (with emphasis on guaranteeing 
safe inter- vehicle distances) will help maximize utilization of airports. The (network 

42 Chapter 3. Applications, Opportunities, and Challenges 

and software) infrastructure for supply chain systems is being built right now, and 
simple automated supply chain management systems are beginning to be deployed. 
In the near future, sophisticated optimization and control methods can be used to 
direct the flow of goods and money between suppliers, assemblers and processors, 
and customers. 

Control over Networks 

While the advances in information technology to date have led to a global Inter- 
net that allows users to exchange information, it is clear that the next phase will 
involve much more interaction with the physical environment. Networks of sen- 
sory or actuator nodes with computational capabilities, connected wirelessly or by 
wires, can form an orchestra which controls our physical environment. Examples 
include automobiles, smart homes, large manufacturing systems, intelligent high- 
ways and networked city services, and enterprise-wide supply and logistics chains. 
Thus, this next phase of the information technology revolution is the convergence of 
communication, computing and control. The following vignette describes a major 
architectural challenge in achieving this convergence. 

Vignette: The importance of abstractions and architecture for the con- 
vergence of communications, computing, and control (P. R. Kumar, 
Univ. of Illinois, Urbana- Champaign) 

Communication networks are very diverse, running over copper, radio, or optical links, 
various computers, routers, etc. However, they have an underlying architecture which 
allows one to just plug-and-play, and not concern oneself with what lies underneath. 
In fact, one reason for the anarchic proliferation of the Internet is precisely this 
architecture — a hierarchy of layers together with peer-to-peer protocols connecting the 
layers at different nodes. On one hand, nodes can be connected to the Internet without 
regard to the physical nature of the communication link, whether it be infrared or cop- 
per, and this is one reason for the tremendous growth in the number of nodes on the 
Internet. On the other hand, the architecture allows plug-and-play at all levels, and thus 
each layer can be designed separately, allowing a protocol at one level to be modified 
over time without simultaneously necessitating a redesign of the whole system. This 
has permitted the Internet protocols to evolve and change over time. 

This raises the issue: What is the right architecture for the convergence of communica- 
tion, control, and computing? Is there an architecture which is application and context 
independent, one which allows proliferation, just as the Open Systems Interconnect 
(OS!) architecture did for communication networks? What are the right abstraction 
layers? How does one integrate information, control, and computation? If the over- 
all design allows us to separate algorithms from architecture, then this convergence of 
control with communication and computation will rapidly proliferate. 

As existing networks continue to build out, and network technology becomes 
cheaper and more reliable than fixed point-to-point connections, even in small lo- 
calized systems, more and more control systems will operate over networks. We 

3.2. Information and Networks 43 

can foresee sensor, actuator, diagnostic, and command and coordination signals all 
traveling over data networks. The estimation and control functions can be dis- 
tributed across multiple processors, also linked by data networks. (For example, 
smart sensors can perform substantial local signal processing before forwarding rel- 
evant information over a network.) 

Current control systems are almost universally based on synchronous, clocked 
systems, so they require communications networks that guarantee delivery of sen- 
sor, actuator, and other signals with a known, fixed delay. While current control 
systems are robust to variations that are included in the design process (such as a 
variation in some aerodynamic coefficient, motor constant, or moment of inertia), 
they are not at all tolerant of (unmodeled) communication delays, or dropped or lost 
sensor or actuator packets. Current control system technology is based on a sim- 
ple communication architecture: all signals travel over synchronous dedicated links, 
with known (or worst-case bounded) delays, and no packet loss. Small dedicated 
communication networks can be configured to meet these demanding specifications 
for control systems, but a very interesting question is: 

Can one develop a theory and practice for control systems that operate 
in a distributed, asynchronous, packet-based environment? 

It is very interesting to compare current control system technology with cur- 
rent packet-based data networks. Data networks are extremely robust to gross, 
unpredicted changes in topology (such as loss of a node or a link) ; packets are sim- 
ply re-sent or re-routed to their destination. Data networks are self-configuring: we 
can add new nodes and links, and soon enough packets are flowing through them. 
One of the amazing attributes of data networks is that, with good architecture and 
protocol design, they can be far more reliable than their components. This is in 
sharp contrast with modern control systems, which are only as reliable as their 
weakest link. Robustness to component failure must be designed in, by hand (and 
is, for safety critical systems). 

Looking forward, we can imagine a marriage of current control systems and 
networks. The goal is an architecture, and design and analysis methods, for dis- 
tributed control systems that operate in a packet-based network. If this is done 
correctly, we might be able to combine the good qualities of a robust control system, 
i.e., high performance and robustness to parameter variation and model mismatch, 
with the good qualities of a network: self-configuring, robust to gross topology 
changes and component failures, and reliability exceeding that of its components. 

One can imagine systems where sensors asynchronously burst packets onto the 
network, control processors process the data and send it out to actuators. Packets 
can be delayed by varying amounts of time, or even lost. Communication links 
can go down, or become congested. Sensors and actuators themselves become un- 
available or available. New sensors, actuators, and processors can be added to the 
system, which automatically reconfigures itself to make use of the new resources. As 
long as there are enough sensors and actuators available, and enough of the packets 
are getting though, the whole system works (although we imagine not as well as 
with a dedicated, synchronous control system). This is of course very different from 
any existing current high performance control system. 

44 Chapter 3. Applications, Opportunities, and Challenges 

It is clear that for some applications, current control methods, based on syn- 
chronous clocked systems and networks that guarantee arrival and bounded delays 
for all communications, are the best choice. There is no reason not to configure 
the controller for a jet engine as it is now, i.e., a synchronous system with guar- 
anteed links between sensors, processors, and actuators. But for consumer appli- 
cations not requiring the absolute highest performance, the added robustness and 
self-reconfiguring abilities of a packet-based control system could make up for the 
lost performance. In any case what will emerge will probably be something in be- 
tween the two extremes, of a totally synchronous system and a totally asynchronous 
packet-based system. 

Clearly, several fundamental control concepts will not make the transition to 
an asynchronous, packet-based environment. The most obvious casualty will be 
the transfer function, and all the other concepts associated with linear time in- 
variant (LTI) systems (impulse and step response, frequency response, spectrum, 
bandwidth, etc.) This is not a small loss, as this has been a foundation of control en- 
gineering since about 1930. With the loss goes a lot of intuition and understanding. 
For example. Bode plots were introduced in the 1930s to understand and design 
feedback amplifiers, were updated to handle discrete-time control systems in the 
1960s, and were applied to robust MIMO control systems in the 1980s (via singular 
value plots). Even the optimal control methods in the 1960s, which appeared at 
first to be quite removed from frequency domain concepts, were shown to be nicely 
interpreted via transfer functions. 

So what methods will make the transition? Many of the methods related 
to optimal control and optimal dynamic resource allocation will likely transpose 
gracefully to an asynchronous, packet-based environment. A related concept that is 
likely to survive is also one of the oldest: Lyapunov functions (which were introduced 
in 1890). The following vignette describes some of the possible changes to control 
that may be required. 

Vignette: Lyapunov Functions in Networked Environments (Stephen 
Boyd, Stanford) 

Here is an example of how an "old" concept from control will update gracefully. The 
idea is that of the Bellman value function, which gives the optimal value of some control 
problem, posed as an optimization problem, as a function of the starting state. It was 
studied by Pontryagin, Bellman, and other pioneers of optimal control in the 1950s, and 
has recently had a resurgence (in generalized form) under the name of control Lyapunov 
function. It is a key concept in dynamic programming. 

The basic idea of a control Lyapunov function (or the Bellman value function) is this: 
If you knew the function, then the best thing to do is to choose current actions that 
minimize the value function in the current step, without any regard for future effects. 
(In other words, we ignore the dynamics of the system.) By doing this we are actually 
carrying out an optimal control for the problem. In other words, the value function is 
the cost function whose greedy minimization actually yields the optimal control for the 
original problem, taking the system dynamics into account. In the work of the 1950s 
and 60s, the value function is just a mathematical stepping stone toward the solution 

3.2. Information and Networks 45 

of optimal control problems. 

But the idea of value function transposes to an asynchronous system very nicely. If 
the value function, or some approximation, were broadcast to the actuators, then each 
actuator could take independent and separate action, i.e., each would do whatever it 
could to decrease the value function. If the actuator were unavailable, then it would do 
nothing. In general the actions of multiple actuators has to be carefully coordinated; 
simple examples show that turning on two feedback systems, each with its own sen- 
sor and actuator, simultaneously, can lead to disastrous loss of performance, or even 
instability. But if there is a value or control Lyapunov function that each is separately 
minimizing, everything is fine; the actions are automatically coordinated (via the value 

Another idea that will gracefully extend to asynchronous packet-based control 
is model predictive control. The basic idea is to carry out far more computation at 
run time, by solving optimization problems in the real-time feedback control law. 
Model predictive control has played a major role in process control, and also in 
supply-chain management, but not (yet) in other areas, mainly owing to the very 
large computational burden it places on the controller implementation. The idea is 
very simple: at each time step we formulate the optimal control problem, up to some 
time horizon in the future, and solve for the whole optimal trajectory (say, using 
quadratic programming). We then use the current optimal input as the actuator 
signal. The sensor signals can be used to update the model, and carry the same 
process out again. A major extension required to apply model predictive control 
in networked environments would be the distributed solution of the underlying 
optimization problem. 

Other Trends in Information and Networks 

While we have concentrated in this section on the role of control in communications 
and networking, there are many problems in the broader field of information science 
and technology for which control ideas will be important. We highlight a few here; 
more information can also be found in a recent National Research Council report 
on embedded systems [32]. 

Vigilant, high confidence software systems Modern information systems are re- 
quired to operate in environments where the users place high confidence on the 
availability and correctness of the software programs. This is increasingly difficult 
due to the networked and often adversarial environment in which these programs 
operate. One approach that is being explored by the computer science community 
is to provide confidence through vigilance. Vigilance refers to continuous, pervasive, 
multi- faceted monitoring and correction of system behavior, i.e., control. 

The key idea in vigilant software is to use fast and accurate sensing to monitor 
the execution of a system or algorithm, compare the performance of the algorithm 
to an embedded model of the computation, and then modify the operation of the 
algorithm (through adjustable parameters) to maintain the desired performance. 


Chapter 3. Applications, Opportunities, and Challenges 




Sort I 





~~^ Feedback 

Figure 3.5. An example of a vigilant high confidence software system: 
distributed sorting using feedback. 

This "sense-compute-act" loop is the basic paradigm of feedback control and pro- 
vides a mechanism for online management of uncertainty. Its power lies in the fact 
that rather than considering every possible situation at design time, the system re- 
acts to specific situations as they occur. An essential element of the strategy is the 
use of either an embedded model, through which an appropriate control action can 
be determined, or a predefined control strategy that is analyzed offline to ensure 
stability, performance, and robustness. 

As an indication of how vigilance might be used to achieve high confidence, 
consider an example of feedback control for distributed sorting, as shown in Fig- 
ure 3.5. We envision a situation in which we have a collection of partial sort algo- 
rithms that are interconnected in a feedback structure. Suppose that each sorter 
has multiple inputs, from which it chooses the best sorted list, and a single output, 
to which it sends an updated list that is more ordered. By connecting these modules 
together in a feedback loop, it is possible to get a completely sorted list at the end 
of a finite number of time steps. 

While imconventional from a traditional computer science perspective, this 
approach gives robustness to failure of individual sorters, as well as self- reconfiguring 
operation. Robustness comes because if an individual module unsorts its data, this 
data will not be selected from the input streams by the other modules. Further, 
if the modules have different loads (perhaps due to other processing being done 
on a given processor), the module with the most time available will automatically 
take on the load in performing the distributed sorting. Other properties such as 
disturbance rejection, performance, and stability could also be studied by using 
tools from control. 

Verification and validation of protocols and software The development of com- 
plex software systems is increasing at a rapid rate and our ability to design such 
systems so that they give provably correct performance is increasingly strained. 
Current methods for verification and validation of software systems require large 
amounts of testing and many errors are not discovered until late stages of develop- 
ment or even product release. Formal methods for verification of software are used 
for systems of moderate complexity, but do not scale well to large software systems. 
Control theory has developed a variety of techniques for giving provably cor- 
rect behavior by using upper and lower bounds to effectively break computational 

3.2. Information and Networks 47 

complexity bounds. Recent results in convex optimization of semialgebraic prob- 
lems (those that can be expressed by polynomial equalities and inequalities) are 
providing new insights into verification of a diverse set of continuous and combi- 
natorial optimization problems [36]. In particular, these new techniques allow a 
systematic search for "simple proofs" of mixed continuous and discrete problems 
and offer ideas for combining formal methods in computer science with stability and 
robustness results in control. 

Real-time supply chain management As increasing numbers of enterprise systems 
are connected to each other across networks, there is an enhanced ability to perform 
enterprise level, dynamic reconfiguration of high availability assets for achieving 
efficient, reliable, predictable operations. As an example of the type of application 
that one can imagine, consider the operation of a network of HVAC systems for a 
regional collection of buildings, under the control of a single operating company. In 
order to minimize overall energy costs for its operation, the company makes a long- 
term arrangement with an energy broker to supply a specified amount of electrical 
power that will be used to heat and cool the buildings. In order to get the best 
price for the energy it purchases, the company agrees to purchase a fixed amount of 
energy across its regional collection of buildings and to pay a premium for energy 
usage above this amount. This gives the energy broker a fixed income as well as a 
fixed (maximum) demand, for which it is willing to sell electricity at a lower price 
(due to less uncertainty in future revenue as well as system loading). 

Due to the uncertainty in the usage of the building, the weather in different 
areas across the region, and the reliability of the HVAC subsystems in the build- 
ings, a key element in implementing such an arrangement is a distributed, real-time 
command and control system capable of performing distributed optimization of 
interconnected assets. The power broker and the company must be able to commu- 
nicate information about asset condition and mission between the control systems 
for their electrical generation and HVAC systems and the subsystems must react 
to sensed changes in the environment (occupancy, weather, equipment status) to 
optimize the fleet level performance of the network. 

Realization of enterprise- wide optimization of this sort will require substantial 
progress in a number of technical areas: distributed, embedded modeling tools that 
allow low resolution modeling of the external system combined with high resolution 
modeling of the local system, resident at each node in the enterprise; distributed 
optimization algorithms that make use of the embedded modeling architecture to 
produce near optimal operations; fault tolerant, networked control systems that 
allow control loops to operate across unreliable network connections; and low cost, 
fault tolerant, reconfigurable hardware and software architectures. 

A very closely related problem is that of C4ISR (command, control, com- 
munications, computers, intelligence, surveillance, and reconnaissance) in military 
systems. Here also, networked systems are revolutionizing the capabilities for con- 
tinuous planning and asset allocation, but new research is needed in providing 
robust solutions that give the required performance in the presence of uncertainty 
and adversaries. The underlying issues and techniques are almost identical to enter- 

48 Chapter 3. Applications, Opportunities, and Challenges 

prise level resource allocation, but the environment in which they must perform is 
much more challenging for military applications. Control concepts will be essential 
tools for providing robust performance in such dynamic, uncertain, and adversarial 

3.3. Robotics and Intelligent Machines 49 

3.3 Robotics and Intelligent Machines 

It is my thesis that the physical functioning of the living individual and the oper- 
ation of some of the newer communication machines are precisely parallel in their 
analogous attempts to control entropy through feedback. Both of them have sensory 
receptors as one stage in their cycle of operation: that is, in both of them there exists 
a special apparatus for collecting information from the outer world at low energy lev- 
els, and for making it available in the operation of the individual or of the machine. 
In both cases these external messages are not taken neat, but through the internal 
transforming powers of the apparatus, whether it be alive or dead. The information 
is then turned into a new form available for the further stages of performance. In 
both the animal and the machine this performance is made to be effective on the 
outer world. In both of them, their performed action on the outer world, and not 
merely their intended action, is reported back to the central regulatory apparatus. 

Norbert Wiener, from The Human Use of Human Beings: Cybernetics and Society, 
1950 [42]. 

Robotics and intelligent machines refer to a collection of applications involv- 
ing the development of machines with human-like behavior. While early robots 
were primarily used for manufacturing, modern robots include wheeled and legged 
machines capable of participating in robotic competitions and exploring planets, 
unmanned aerial vehicles for surveillance and combat, and medical devices that 
provide new capabilities to doctors. Future applications will involve both increased 
autonomy and increased interaction with humans and with society. Control is a 
central element in all of these applications and will be even more important as the 
next generation of intelligent machines are developed. 

Background and History 

The goal of cybernetic engineering, already articulated in the 1940s and even be- 
fore, has been to implement systems capable of exhibiting highly flexible or "in- 
telligent" responses to changing circumstances. In 1948, the MIT mathematician 
Norbert Wiener gave a widely read, albeit completely non-mathematical, account 
of cybernetics [41]. A more mathematical treatment of the elements of engineering 
cybernetics was presented by H. S. Tsien in 1954, driven by problems related to 
control of missiles [40]. Together, these works and others of that time form much 
of the intellectual basis for modern work in robotics and control. 

The early applications leading up to today's robotic systems began after World 
War II with the development of remotely controlled mechanical manipulators, which 
used master-slave mechanisms. Industrial robots followed shortly thereafter, start- 
ing with early innovations in computer numerically controlled (CNC) machine tools. 
Unimation, one of the early robotics companies, installed its first robot in a General 
Motors plant in 1961. Sensory systems were added to allow robots to respond to 
changes in their environment and by the 1960s many new robots were capable of 
grasping, walking, seeing (through binary vision), and even responding to simple 
voice commands. 

The 1970s and 80s saw the advent of computer controlled robots and the 
field of robotics became a fertile ground for research in computer science and me- 


Chapter 3. Applications, Opportunities, and Challenges 


Figure 3.6. (a) The Mars Sojourner and (h) Sony AIBO robots. Pho- 
tographs courtesy of Jet Propulsion Laboratory and Sony. 

chanical engineering. Manufacturing robots became commonplace (led by Japanese 
companies) and a variety of tasks ranging from mundane to high precision, were un- 
dertaken with machines. Artificial intelligence (AI) techniques were also developed 
to allow higher level reasoning, including attempts at interaction with humans. At 
about this same time, new research was undertaken in mobile robots for use on the 
factory floor and remote environments. 

Two accomplishments that demonstrate the successes of the field are the Mars 
Sojourner robot and the Sony AIBO robot, shown in Figure 3.6. Sojourner success- 
fully maneuvered on the surface of Mars for 83 days starting in July 1997 and sent 
back live pictures of its environment. The Sony AIBO robot debuted in June of 
1999 and was the first "entertainment" robot that was mass marketed by a major 
international corporation. It was particularly noteworthy because of its use of AI 
technologies that allowed it to act in response to external stimulation and its own 

It is interesting to note some of the history of the control community in 
robotics. The IEEE Robotics and Automation Society was jointly founded in the 
early 1980s by the Control Systems Society and the Computer Society, indicating 
the mutual interest in robotics by these two communities. Unfortunately, while 
many control researchers were active in robotics, the control community did not 
play a leading role in robotics research throughout much of the 1980s and 90s. 
This was a missed opportunity since robotics represents an important collection 
of applications that combines ideas from computer science, artificial intelligence, 
and control. New applications in (unmanned) flight control, underwater vehicles, 
and satellite systems are generating renewed interest in robotics and many control 
researchers are becoming active in this area. 

Despite the enormous progress in robotics over the last half century, the field 
is very much in its infancy. Today's robots still exhibit extremely simple behaviors 
compared with humans and their ability to locomote, interpret complex sensory 

3.3. Robotics and Intelligent Machines 51 

inputs, perform higher level reasoning, and cooperate together in teams is limited. 
Indeed, much of Wiener's vision for robotics and intelligent machines remains unre- 
alized. While advances are needed in many fields to achieve this vision — including 
advances in sensing, actuation, and energy storage — the opportunity to combine 
the advances of the Al community in planning, adaptation, and learning with the 
techniques in the control community for modeling, analysis, and design of feedback 
systems presents a renewed path for progress. This application area is strongly 
linked with the Panel's recommendations on the integration of computing, commu- 
nication and control, development of tools for higher level reasoning and decision 
making, and maintaining a strong theory base and interaction with mathematics. 

Challenges and Future Needs 

The basic electromechanical engineering and computing capabilities required to 
build practical robotic systems have evolved over the last half-century to the point 
where today there exist rapidly expanding possibilities for making progress toward 
the long held goals of intelligence and autonomy. The implementation of principled 
and moderately sophisticated algorithms is already possible on available computing 
hardware and more capability will be here soon. The successful demonstration of 
vision guided automobiles operating at high speed, the use of robotic devices in 
manufacturing, and the commercialization of mobile robotic devices attest to the 
practicality of this field. 

Robotics is a broad field; the perspectives afforded by computer science, con- 
trol, electrical engineering, mechanical engineering, psychology, and neuroscience 
all yield important insights. Even so, there are pervasive common threads, such as 
the understanding and control of spatial relations and their time evolution. The 
emergence of the field of robotics has provided the occasion to analyze, and to at- 
tempt to replicate, the patterns of movement required to accomplish useful tasks. 
On the whole, this has been a sobering experience. Just as the ever closer exam- 
ination of the physical world occasionally reveals inadequacies in our vocabulary 
and mathematics, roboticists have found that it is quite awkward to give precise, 
succinct descriptions of effective movements using the syntax and semantics in com- 
mon use. Because the motion generated by a robot is usually its raison d'etre, it 
is logical to regard motion control as being a central problem. Its study has raised 
several new questions for the control engineer relating to the major themes of feed- 
back, stability, optimization, and estimation. For example, at what level of detail in 
modeling (i.e. kinematic or dynamic, linear or nonlinear, deterministic or stochas- 
tic, etc.) does optimization enter in a meaningful way? Questions of coordination, 
sensitivity reduction, stability, etc. all arise. 

In addition to these themes, there is the need for development of appropriate 
software for controlling the motion of these machines. At present there is almost no 
transportability of robotic motion control languages. The idea of vendor indepen- 
dent languages that apply with no change to a wide range of computing platforms 
and peripherals has not yet been made to work in the field of robotics. The clear 
success of such notions when applied to operating systems, languages, networks, 
disk drives, and printers makes it clear that this is a major stumbling block. What 

52 Chapter 3. Applications, Opportunities, and Challenges 

is missing is a consensus about how one should structure and standardize a "motion 
description language." Such a language should, in addition to other things, allow 
one to implement compliance control in a general and natural way. 

Another major area of study is adaptation and learning. As robots become 
more commonplace, they will need to become more sophisticated in the way they 
interact with their environment and reason about the actions of themselves and 
others. The robots of science fiction are able to learn from past experience, interact 
with humans in a manner that is dependent on the situation, and reason about high 
level concepts to which they have not been previously exposed. In order to achieve 
the vision of intelligent machines that are common in our society, major advances in 
machine learning and cognitive systems will be required. Robotics provides an ideal 
testbed for such advances: applications in remote surveillance, search and rescue, 
entertainment, and personal assistance are all fertile areas for driving forward the 
state of the art. 

In addition to better understanding the actions of individual robots, there 
is also considerable interest and opportunity in cooperative control of teams of 
robots. The U.S. military is considering the use of multiple vehicles operating in a 
coordinated fashion for surveillance, logistical support, and combat, to offload the 
burden of dirty, dangerous, and dull missions from humans. Over the past decade, 
several new competitions have been developed in which teams of robots compete 
against each other to explore these concepts. Perhaps the best known of these is 
RoboCup, which is described briefly in the following vignette. 

Vignette: RoboCup — A testbed for autonomous collaborative behavior 
in adversarial environments (RafFaello D'Andrea, Cornell University) 

RoboCup is an international collection of robotics and artificial intelligence (A!) compe- 
titions. The competitions are fully autonomous (no human intervention) head-to-head 
games, whose rules are loosely modeled after the human game of soccer; each team 
must attempt to score more goals than the opponent, subject to well defined rules 
and regulations (such as size restrictions, collision avoidance, etc.) The three main 
competitions are known as the Simulation League, the F2000 League, and the F180 

The F180 League is played by 6 inch cube robots on a 2 by 3 meter table (see Figure 3.7, 
and can be augmented by a global vision system; the addition of global vision shifts 
the emphasis away from object localization and computer vision, to collaborative team 
strategies and aggressive robot maneuvers. In what follows, we will describe Cornell's 
experience in the F180 League at the 1999 competition in Stockholm, Sweden and the 
2000 competition in Melbourne, Australia. 

Cornell was the winner of the F180 League in both 1999, the first year it entered the 
competition, and 2000. The team's success can be directly attributed to the adoption 
of a systems engineering approach to the problem, and by emphasizing system dynamics 
and control. The systems engineering approach was instrumental in the complete devel- 
opment of a competitive team in only 9 months (for the 1999 competition). Twenty-five 
students, a mix of first year graduate students and seniors representing computer sci- 

3.3. Robotics and Intelligent Machines 


Figure 3.7. F180 league RoboCup soccer. Photograph courtesy Raffaello 
D 'Andrea. 

ence, electrical engineering, and mechanical engineering, were able to construct two 
fully operational teams by effective project management, by being able to capture the 
system requirements at an early stage, and by being able to cross disciplinary boundaries 
and communicate among themselves. A hierarchical decomposition was the means by 
which the problem complexity was rendered tractable; in particular, the system was 
decomposed into estimation and prediction, real time trajectory generation and control, 
and high level strategy. 

Estimation and prediction entailed relatively simple concepts from filtering, tools known 
to most graduate students in the area of control. In particular, smoothing filters for 
the vision data and feedforward estimators to cope with system latency were used to 
provide an accurate and robust assessment of the game state. Trajectory generation 
and control consisted of a set of primitives that generated feasible robot trajectories; 
various relaxation techniques were used to generate trajectories that (1) could quickly 
be computed in real time (typically less than 1000 floating point operations), and (2) 
took full advantage of the inherent dynamics of the vehicles. In particular, feasible 
but aggressive trajectories could quickly be generated by solving various relaxations of 
optimal control problems. These primitives were then used by the high level strategy, 
essentially a large state-machine. 

The high-level strategy was by far the most ad-hoc and heuristic component of the 

54 Chapter 3. Applications, Opportunities, and Challenges 

Cornell RoboCup team. The various functions that determined whether passes and 
interceptions were possible were rigorous, in the sense that they called upon the provably 
effective trajectory and control primitives, but the high level strategies that determined 
whether a transition from defense to offense should be made, for example, or what play 
should be executed, relied heavily on human judgment and observation. As of March 
2001, most of the efforts at Cornell have shifted to understanding how the design and 
verification of high level strategies that respect and fully utilize the system dynamics 
can take place. 

Certain robotic applications, such as those that call for the use of vision sys- 
tems to guide robots, now require the use of computing, communication and control 
in an integrated way. The computing that is to be done must be opportunistic, i.e. 
it must be tailored to fit the needs of the specific situation being encountered. The 
data compression that is needed to transmit television signals to a computer must 
be done with a view toward how the results will be used by the control system. It 
is both technologically difficult and potentially dangerous to build complex systems 
that are controlled in a completely centralized way. For this reason we need to de- 
cide how to distribute the control function over the communication system. Recent 
work on the theory of communication protocols has made available better methods 
for designing efficient distributed algorithms. This work can likely be adapted in 
such a way as to serve the needs of robotic applications. 

Finally, we note the need to develop robots that can operate in highly unstruc- 
tured environments. This will require considerable advances in visual processing and 
understanding, complex reasoning and learning, and dynamic motion planning and 
control. Indeed, a framework for reasoning and planning in these unstructured en- 
vironments will likely require new mathematical concepts that combine dynamics, 
logic, and geometry in ways that are not currently available. One of the major ap- 
plications of such activities is in the area of remote exploration (of the earth, other 
planets, and the solar system), where human proxies will be used for continuous 
exploration to expand our understanding of the universe. 

Other Trends in Robotics and Intelligent Machines 

In addition to the challenges and opportunities described above, there are many 
other trends that are important for advances in robotics and intelligent machines 
and that will drive new research in control. 

Mixed Initiative Systems and Human Interfaces It seems clear that more exten- 
sive use of computer control, be it for factories, automobiles or homes, will be most 
effective if it comes with a natural human interface. Having this goal in mind, one 
should look for interfaces which are not only suitable for the given application but 
which are sufficiently general so that, with minor modification, they can serve in 
related applications as well. Progress in this area will not only require new insights 
into processing of visual data (described above) , but a better understanding of the 
interactions of humans with machines and computer controlled systems. 

3.3. Robotics and Intelligent Machines 55 

One program underway in the United States is exploring the use of "variable 
autonomy" systems , in which machines controlled by humans are given varying 
levels of command authority as the task evolves. Such systems involve humans 
that are integrated with a computer-controlled system in such a way that the hu- 
mans may be simultaneously receiving instructions from and giving instructions to 
a collection of machines. One application of this concept is a semi-automated air 
traffic control system, in which command and control computers, human air traffic 
controllers, ffight navigation systems, and pilots have varying levels of responsibil- 
ity for controlling the airspace . Such a system has the possibility of combining 
the strengths of machines in rapid data processing with the strengths of humans 
in complex reasoning , but will require substantial advances in understanding of 
man-machine systems. 

Control Using High Data-Rate Sensors Without large expenditure, we are able 
to gather and store more pictures and sounds, temperatures and particle counts, 
than we know how to use. We continue to witness occasional catastrophic fail- 
ures of our man-machine systems, such as those used for transportation, because 
we do not correctly interpret or appropriately act on the information available to 
us. It is apparent that in many situations collecting the information is the easy 
part. Feedback control embodies the idea that performance can be improved by 
coupling measurement directly to action. Physiology provides many examples at- 
testing to the effectiveness of this technique. However, as engineers and scientists 
turn their attention to the highly automated systems currently being built by the 
more advanced manufacturing and service industries, they often find that the direct 
application of feedback control is frustrated by a web of interactions which make 
the smallest conceptual unit too complex for the usual type of analysis. In partic- 
ular, vision guided systems are difficult to design and often fail to be robust with 
respect to lighting conditions and changes in the environment. In order to proceed, 
it seems, design and performance evaluation must make more explicit use of ideas 
such as adaptation, self-configuration , and self-optimization. 

Indications are that the solution to the problems raised above will involve 
active feedback control of the perceptual processes, an approach which is common- 
place in biology. One area that has received considerable attention is the area of 
active vision in which the vision sensor is controlled on the basis of the data it gen- 
erates. Other work involves tuning the vision processing algorithms on basis of the 
data collected. The significant progress now being made toward the resolution of 
some of the basic problems results, in large part, from the discovery and aggressive 
use of highly nonlinear signal processing techniques. Examples include the varia- 
tional theories that have been brought to bear on the image segmentation problem, 
the theories of learning based on computational complexity, and information theo- 
retic based approaches to perceptual problems. Attempts to incorporate perceptual 
modules into larger systems, however, often raise problems about communication 
and distributed computation which are not yet solved. 

Related to this is the problem of understanding and interpreting visual data. 
The technology for recognizing voice commands is now sophisticated enough to see 

56 Chapter 3. Applications, Opportunities, and Challenges 

use in many commercial systems. However, the processing and interpretation of 
image data is in its infancy, with very few systems capable of decision making and 
action based on visual data. One specific example is understanding of human mo- 
tion, which has many applications in robotics. While it is possible for robots to react 
to simple gestures, we do not yet have a method for describing and reasoning about 
more complex motions, such as a person walking down the street, stooping to pick 
up a penny, and being bumped by someone that did not see them stop. This sort of 
interpretation requires representation of complex spatial and symbolic relationships 
that are beyond currently available tools in areas such as system identification, state 
estimation, and signal to symbol translation. 

Medical Robotics Computer and robotic technology is having a revolutionary im- 
pact on the practice of medical surgery. By extending surgeons' ability to plan and 
carry out surgical interventions more accurately and in a minimally invasive manner, 
computer-aided and robotic surgical systems can reduce surgical and hospital costs, 
improve clinical outcomes, and improve the efficiency of health care delivery. The 
ability to consistently carry out surgical procedures and to comprehensively log key 
patient and procedure outcome data should also lead to long term improvements in 
surgical practice. 

Robotic technology is useful in a variety of surgical contexts. For example, the 
"Robodoc" surgical assistant uses the precision positioning and drilling capabilities 
of robots to improve the fit of implants during total hip replacement [4]. The 
improved fit leads to significantly fewer complications and longer lasting implants. 
Similarly, 3-dimensional imaging data can drive the precision movement of robot 
arms during stereotactical brain surgery, thereby reducing the risk of collateral brain 
damage. The DaVinci system from Intuitive Surgical uses teleoperation and force- 
refiecting feedback methods to enable minimally invasive coronary procedures that 
would otherwise require massively invasive chest incisions [31]. Figure 3.8 shows 
the ZEUS system developed by Computer Motion, Inc. a modified version of which 
was used in 2001 to allow a surgeon in New York to operate on a 68 year old woman 
in Strasbourg, France [26]. These are only a few of the currently approved robotic 
surgical systems, with many, many more systems in clinical trials and laboratory 

While medical robotics is becoming a reality, there are still many open research 
and development questions. Clearly, medical robotics will benefit from the same 
future advances in computing, communication, sensing, and actuation technology 
that will broadly impact all future control systems. However, the issue of system and 
software reliability is fundamental to the future of medical robotics. Formal methods 
for system verification of these highly nonlinear, hybrid, and uncertain systems, as 
well as strategies for extreme fault tolerance are clearly needed to ensure rapid 
and widespread adoption of these technologies. Additionally, for the foreseeable 
future, robotic medical devices will be assistants to human surgeons. Consequently, 
their human/machine interfaces must be able to deal with the complex contexts of 
crowded operating rooms in an absolutely reliable way, even during unpredictable 
surgical events. 

3.3. Robotics and Intelligent Machines 


Figure 3.8. The ZEUS (tm) Robotic Surgical System, developed by Com- 
puter Motion Inc., is capable of performing minimally invasive microsurgery proce- 
dures from a remote location. Photograph courtesy of Computer Motion Inc. 

58 Chapter 3. Applications, Opportunities, and Challenges 

3.4 Biology and Medicine 

Feedback is a central feature of life. The process of feedback governs how we grow, 
respond to stress and challenge, and regulate factors such as body temperature, blood 
pressure, and cholesterol level. The mechanisms operate at every level, from the 
interaction of proteins in cells to the interaction of organisms in complex ecologies. 

Mahlon B. Hoagland and B. Dodson, from The Way Life Works, 1995 [17]. 

At a variety of levels of organization — from molecular to cellular to organismal — 
biology is becoming more accessible to approaches that are commonly used in 
engineering: mathematical modeling, systems theory, computation, and abstract 
approaches to synthesis. Conversely, the accelerating pace of discovery in biologi- 
cal science is suggesting new design principles that may have important practical 
applications in man-made systems. This synergy at the interface of biology and 
engineering offers unprecedented opportunities to meet challenges in both areas. 
The principles of control are central to many of the key questions in biological 
engineering and will play a enabling role in the future of this field. 

A major theme identified by the Panel was the science of reverse (and eventu- 
ally forward) engineering of biological control networks. There are a wide variety of 
biological phenomena that provide a rich source of examples for control, including 
gene regulation and signal transduction; hormonal, immunological, and cardiovas- 
cular feedback mechanisms; muscular control and locomotion; active sensing, vision, 
and proprioception; attention and consciousness; and population dynamics and epi- 
demics. Each of these (and many more) provide opportunities to figure out what 
works, how it works, and what can be done to affect it. 

The Panel also identified potential roles for control in medicine and biomedical 
research. These included intelligent operating rooms and hospitals, from raw data to 
decisions; image guided surgery and therapy; hardware and soft tissue integration; 
fluid flow control for medicine and biological assays; and the development of physical 
and neural prosthesis. Many of these areas have substantial overlap with robotics 
and some have been discussed already in Section 3.3. 

We focus in this section on three interrelated aspects of biological systems: 
molecular biology, integrative biology, and medical imaging. These areas are rep- 
resentative of a larger class of biological systems and demonstrate how principles 
from control can be used to understand nature and to build engineered systems. 

Molecular Biology^ 

The life sciences are in the midst of a major revolution that will have fundamental 
implications in biological knowledge and medicine. Genomics has as its objective 
the complete decoding of DNA sequences, providing a "parts list" for the proteins 
present in every cell of the organism being studied. Proteomics is the study of 
the three-dimensional structure of these complex proteins. The shape of a protein 
determines its function: proteins interact with each other through "lego-like" fitting 

^The Panel would like to thank Eduardo Sontag for his contributions to this section, based on 
his Reid Prize plenary lecture at the 2001 SIAM Annual Meeting. 

3.4. Biology and Medicine 


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^m GSK-3|I 


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APC ,■ 
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^,-»- CdC42 -*• P1 3K >■ Rac 


Growth Factors _ 

(e.g. Bombesin) -»- (7-TMR) - 
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(e.g. IGFl) V -J- 

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CdC42 *■ Rac ->■ Rho 

"-G-Prot-»-AdCycl-^ PKA 

Figure 3.9. The wiring diagram of the growth signaling circuitry of the 
mammalian cell [16]. 

of parts in "lock; and k;ey" fashion, and their conformation also enhances or represses 
DNA expression through selective binding. 

One may view cell life as a huge "wireless" network of interactions among 
proteins, DNA, and smaller molecules involved in signaling and energy transfer. As 
a large system, the external inputs to a cell include physical signals (UV radiation, 
temperature) as well as chemical signals (drugs, hormones, nutrients). Its outputs 
include chemicals that affect other cells. Each cell can be thought of, in turn, as 
composed of a large number of subsystems involved in cell growth, maintenance, 
division, and death. A typical diagram describing this complex set of interactions 
is shown in Figure 3.9. 

The study of cell networks leads to the formulation of a large number of ques- 
tions. For example, what is special about the information-processing capabilities, 
or input/output behaviors, of such biological networks? Can one characterize these 
behaviors in terms familiar to control theory (e.g., transfer functions or Volterra se- 
ries)? What "modules" appear repeatedly in cellular signaling cascades, and what 
are their system-theoretic properties? Inverse or "reverse engineering" issues in- 
clude the estimation of system parameters (such as reaction constants) as well as 
the estimation of state variables (concentration of protein, RNA, and other chemical 
substances) from input/output experiments. Generically, these questions may be 
viewed respectively as the identification and observer (or filtering) problems which 
are at the center of much of control theory. 

One can also attempt to better understand the stability properties of the 

60 Chapter 3. Applications, Opportunities, and Challenges 

various cascades and feedback loops that appear in cellular signaling networks. Dy- 
namical properties such as stability and existence of oscillations in such networks are 
of interest, and techniques from control theory such as the calculation of robustness 
margins will play a central role in the future. At a more speculative (but increasingly 
realistic) level, one wishes to study the possibility of using control strategies (both 
open and closed loop) for therapeutic purposes, such as drug dosage scheduling. 

The need for mathematical models in cellular biology has long been recognized, 
and indeed many of the questions mentioned above have been studied for the last 20 
or 30 years. What makes the present time special is the availability of huge amounts 
of data — generated by the genomics and proteomics projects, and research efforts in 
characterization of signaling networks — as well as the possibility for experimental 
design afforded by genetic engineering tools (gene knock-outs and insertions, PCR) 
and measurement technology (green fluorescent protein and other reporters, and 
gene arrays) . Control-oriented modeling and analysis of feedback interconnections 
is an integral component of building effective models of biological systems. 

Feedback and uncertainty. From a theoretical perspective, feedback serves to min- 
imize uncertainty and increase accuracy in the presence of noise. The cellular en- 
vironment is extremely noisy in many ways, while at the same time variations in 
levels of certain chemicals (such as transcriptional regulators) may be lethal to the 
cell. Feedback loops are omnipresent in the cell and help regulate the appropriate 
variations. It is estimated, for example, that in E. coli about 40% of transcription 
factors self-regulate. One may ask whether the role of these feedback loops is in- 
deed that of reducing variability, as expected from principles of feedback theory. 
Recent work tested this hypothesis in the context of tetracycline repressor protein 
(TetR) [7]. An experiment was designed in which feedback loops in TetR produc- 
tion were modified by genetic engineering techniques, and the increase in variability 
of gene expression was correlated with lower feedback "gains," verifying the role 
of feedback in reducing the effects of uncertainty. Modern experimental techniques 
will afford the opportunity for testing experimentally (and quantitatively) other 
theoretical predictions, and this may be expected to be an active area of study at 
the intersection of control theory and molecular biology. 

Necessity of embedded structures in regulation loops. Another illustration of the 
interface between feedback theory and modern molecular biology is provided by 
recent work on chemotaxis in bacterial motion. E. coli moves, propelled by fiagella, 
in response to gradients of chemical attractants or repellents, performing two basic 
types of motions: tumbles (erratic turns, with little net displacement) and runs. In 
this process, E. coli carries out a stochastic gradient search strategy: when sensing 
increased concentrations it stops tumbling (and keeps running), but when it detects 
low gradients it resumes tumbling motions (one might say that the bacterium goes 
into "search mode" ) . 

The chemotactic signaling system, which detects chemicals and directs mo- 
tor actions, behaves roughly as follows: after a transient nonzero signal ("stop 
tumbling, run toward food"), issued in response to a change in concentration, the 
system adapts and its signal to the motor system converges to zero ("OK, tum- 
ble"). This adaptation happens for any constant nutrient level, even over large 

3.4. Biology and Medicine 61 

ranges of scale and system parameters, and may be interpreted as robust (struc- 
turally stable) rejection of constant disturbances. The internal model principle of 
control theory implies (under appropriate technical conditions) that there must be 
an embedded integral controller whenever robust constant disturbance rejection is 
achieved. Recent models and experiments succeeded in finding, indeed, this embed- 
ded structure [5, 43]. 

This work is only one of the many possible uses of control theoretic knowledge 
in reverse engineering of cellular behavior. Some of the deepest parts of the theory 
concern the necessary existence of embedded control structures, and in this man- 
ner one may expect the theory to suggest appropriate mechanisms and validation 
experiments for them. 

Genetic circuits. Biomolecular systems provide a natural example of hybrid systems, 
which combine discrete and logical operations (a gene is either turned on or off for 
transcription) and continuous quantities (such as concentrations of chemicals) in a 
given cell or in a cell population). Complete hybrid models of basic circuits have 
been formulated, such as the lysogeny/lysis decision circuit in bacteriophage A [28] . 
Current research along these lines concerns itself with the identification of 
other naturally occurring circuits, as well as with the engineering goal of designing 
circuits to be incorporated into delivery vehicles (bacteria, for example), for ther- 
apeutic purposes. This last goal is, in principle, mathematically in the scope of 
realization theory, that branch of systems theory which deals with the synthesis of 
dynamical systems which implement a specified behavior. 

Integrative Biology^ 

Control also has a role to play in understanding larger scale organisms, such as 
insects and animals. The components of these integrative biological systems are be- 
coming much better understood and, like molecular systems, it is becoming evident 
that systems principles are required to build the next level of understanding. This 
understanding of natural systems will enable new approaches to engineered systems, 
as we begin to build systems with the efficiency, robustness, and versatility of the 
natural world. We focus here on the problem of locomotion, for which there has 
been substantial recent work (see [13] for a review). 

Integrative studies of locomotion have revealed several general principles that 
underly a wide variety of organisms. These include energy storage and exchange 
mechanisms in legged locomotion and swimming, nonpropulsive lateral forces that 
benefit stability and maneuverability, and locomotor control systems that combine 
mechanical reflexes with multimodal sensory feedback and feedforward control. Lo- 
comotion, especially at the extremes of what is found in nature, provides a rich set 
of examples that have helped elucidate a variety of structure- function relationships 
in biological systems. 

Control systems and feedback play a central role in locomotion. A suite of 
neurosensory devices are used within the musculoskeletal system and are active 
throughout each cycle of locomotion. In addition, the viscoleastic dynamics of the 

^The Panel would like to thank Michael Dickinson for his contributions to this section. 


Chapter 3. Applications, Opportunities, and Challenges 

molor codo 

wing & body 



_ sensory 

forces & 

Uiroiitih space 

A muscles & A acrn- A hndy A 

J skeleton vJ dynamics J'' dyjiamics \J 


Figure 3.10. Overview of flight behavior in a fruit fly, Drosophila. (a) 
Cartoon of the adult fruit fly showing the three major sensor strictures used in 
flight: eyes, antennae, and halteres (detect angular rotations) . (b) Example flight 
trajectories over a 1 meter circular arena, with and without internal targets, (c) 
A schematic control model of the flight system. Figure and description courtesy of 
Michael Dickinson. 

musculoskeletal system play a critical role in providing rapid feedback paths that 
enable stable operation. Rapid feedback from both mechanical and neural pathways 
is integrated with information from eyes, ears, noses and other sensing organs used 
to control the overall motion of an animal and provide robust operation in a wide 
variety of environments. 

The process that gives rise to locomotion is a complex one, as illustrated 
in Figure 3.10 for the flight behavior of a fruit fly. Each element of the flight 
control system has enormous complexity in itself, with the interconnection (grossly 
simplified in the figure) allowing for a very rich set of behaviors. The sensors, 
actuators, and control systems for insects such as the fly are highly evolved, so 
that the dynamics of the system play strongly into the overall capabilities of the 

From the perspective of control theory, the performance, robustness, and fault 
tolerance of the fly's flight control system represents a gold standard by which all 

3.4. Biology and Medicine 63 

autonomous systems might be judged. Flies can manage to stay in the air with 
torn wings, missing legs, blind eyes, or when burdened with twice their weight in 
additional mass. The fact that the control algorithms yielding this behavior reside in 
a brain the size of a sesame seed raises the bar for any biomimetic effort attempting 
to match its performance. If the principles that engender a fly with such robust 
agility could be discovered and formalized for general use, the results might catalyze 
a revolution in the design, fabrication, and implementation of control systems. 

Similarly, the use of control tools to understand the fly's flight control system 
represents a systems approach to biology that will be important for understand- 
ing the general principles of locomotion systems and allow new understanding of 
integrative biological principles. 

This synergy between biology and control in insect flight is but one example of 
many that are possible and that form a rich source of scientific and engineering ac- 
tivity. Additional areas of overlap include multiresolution modeling and analysis of 
(nongeneric, designed, evolved, heterogeneous) multiscale systems, and integrated 
communications and computing for control of and with pervasive, distributed, em- 
bedded networks. Biological systems are also exceptionally capable of transforming 
raw data into information and knowledge, and eventually into decision and action. 
These are precisely the problems that confront us in building engineering systems 
and the interaction of biologists and control researchers is destined to be fruitful. 

Medical Imaging^ 

Control is also an essential element in the burgeoning field of biomedicine. Some of 
these applications, such as robot surgery, have already been discussed in the context 
of robotics and intelligent machines (see Section 3.3). We consider two additional 
examples here: image guided therapy (IGT) and image guided surgery (IGS). 

Image guided therapy and surgery provide illustrations of how to use biomed- 
ical engineering principles to develop general-purpose software methods that can 
be integrated into complete therapy delivery systems. Such systems will support 
more effective delivery of many image-guided procedures — biopsy, minimally inva- 
sive surgery, and radiation therapy, among others. A key element is controlled active 
vision. To understand the its role in the therapeutic process, and to appreciate the 
current usage of images before, during, and after treatment, one must consider 
the four main components of IGT and IGS: localization, targeting, monitoring and 

To use controlled active imaging one must first develop robust algorithms 
for segmentation, automated methods that create patient-specific models of rele- 
vant anatomy from multimodal imagery, and registration, automated methods that 
align multiple data sets with each other and with the patient. These technologies 
must then be integrated into complete and coherent image guided therapy delivery 
systems and validated using performance measures established in particular ap- 
plication areas. Control enters at almost every stage of the process. For example, 
control-theoretic methods can be essential for the success of the deformable or active 

*The Panel would like to thank Allen Tannonbaum for his contributions to this section. 

64 Chapter 3. Applications, Opportunities, and Challenges 

contours technique in active vision for therapeutic and surgical procedures. These 
are autonomous processes that employ image coherence in order to track features 
of interest over time. They have been used for segmentation and edge detection as 
well. For dynamically changing imagery in a surgical environment, Kalman filtering 
has been important in estimating the position of an active contour at a given time 
given its previous position. This estimated data may be used then in a closed loop 
visual tracker. 

Further, speed and robustness are very important in interventional magnetics, 
which uses magnetic resonance imagery (MRI) during surgery. Here surgeons can 
operate in an open MRI device, and use the images to guide their procedure. Fast 
segmentation is of paramount importance, and one can use active contours very 
effectively when coupled with an estimation scheme to extract key features (such 
as a brain tumor or breast cyst). 

Image registration is the process of establishing a common geometric reference 
frame between two or more data sets from the same or different imaging modalities 
possibly taken at different times. Multimodal registration proceeds in several steps. 
First, each image or data set to be matched should be individually calibrated, 
corrected from imaging distortions, and cleaned from noise and imaging artifacts. 
Next, a measure of dissimilarity between the data sets must be established, so we 
can quantify how close an image is from another after transformations are applied 
to them. Once features have been extracted from each image, they must be paired 
to each other. Then, a similarity measure between the paired features is formulated 
which can be formulated as an optimization problem of the type many times used 
in control. 

Optimal transport methods have proved very useful for this. Optimal trans- 
port ideas have been used in nonlinear stability analysis, and very similar concepts 
lead to a measure of similarity between images which can be employed in registration 
and data fusion. 

In general, IGT and IGS will benefit enormously from systems oriented ideas. 
At this point most of the control is being done by the computer vision and med- 
ical imaging community. By building stronger ties between these groups and the 
control community, it will be possible to make more rapid progress and to leverage 
advances from other applications. In addition, the specific features of this class of 
problems will drive new advances in control theory and technology, which can then 
be exported to other areas. 

3.5. Materials and Processing 



Figure 3.11. (a) Intel Pentium IV wafer and (h) die. Photographs cour- 
tesy of Intel. 

3.5 Materials and Processing^ 

The chemical industry is among the most successful industries in the United States, 
producing $400 billion of products annually and providing over one million U.S. 
jobs. Having recorded a trade surplus for forty consecutive years, it is the country's 
premier exporting industry: chemical industry exports totaled $72.5 billion in 2000, 
accounting for more than 10% of all U.S. exports, and generated a record trade 
surplus in excess of $20 billion in 1997. 

Process manufacturing operations will require a continual infusion of advanced 
information and process control technologies if the chemical industry is to maintain 
its global ability to deliver products that best serve the customer reliably at the 
lowest cost. In addition, a number of new technology areas are being explored that 
will require new approaches to control in order to be successful. These range from 
nanotechnology in areas such as electronics, chemistry, and biomaterials, to thin 
film processing and design of integrated microsystems, to supply chain management 
and enterprise resource allocation. The payoffs for new advances in these areas are 
substantial, and the use of control is critical to future progress in sectors from 
semiconductors to pharmaceuticals to bulk materials. 

Background and History 

At least one materials or chemicals process is involved in the manufacture of nearly 
every commercial product, including microprocessors, consumer products such as 
detergents and shampoo, books, diskettes, disk drives, video cassette recorders. 

^Thc Panel would like to thank Richard Braatz and Frank Doyle for their eontributions to this 

66 Chapter 3. Applications, Opportunities, and Challenges 

food, pharmaceuticals, adliesives, automobile dashboards, and aircraft interiors. 
Feedback controllers for these processes provide improved product quality, reduced 
materials and energy usage, reduced environmental impact, improved safety, and 
the reduced costs needed for U.S. industry to be competitive in the global economy. 

By the late 1960s, process control had been implemented liberally to chem- 
ical and materials processes, primarily in the form of single-loop controllers with 
little communications between controllers. Multi-variable control began to be im- 
plemented in the 1970s, including some rather large scale processes such as the 
control of uniformity in plastic film and paper machines. The use of multi-variable 
control grew rapidly throughout the 1980s and 1990s. Over the last 25 years, 
niulti- variable optimal control in the form of model predictive control has become 
a standard control technique in the process industries for addressing actuator and 
state constraints, which are quite prevalent in chemicals and materials processes. 
Model predictive control explicitly takes constraints into account during the online 
calculation of the manipulated variable actions. In 2000, more than 5000 applica- 
tions of model predictive control were reported by the control vendors of that time 
(e.g., Adersa, Aspen Technology, Honeywell Hi-Spec, Invensys, and Shell Global 
Solutions) [38]. Applications have been reported in a wide range of industries in- 
cluding refining, petrochemical, pulp and paper, air separation, food processing, 
furnaces, aerospace, and automotive. In recent years model predictive control algo- 
rithms have been developed that enable their application to very large scale process 
control problems. 

This should not be taken, however, to indicate that all process control prob- 
lems have been solved. New control techniques are needed that address all of the 
characteristics of the most challenging chemicals and materials processes. 

Current Challenges and Future Needs 

The Panel identified a number of common features within materials and processing 
that pervade many of the applications. Modeling plays a crucial role and there is 
a clear need for better solution methods for multidisciplinary systems combining 
chemistry, fluid mechanics, thermal sciences and other disciplines at a variety of 
temporal and spatial scales. Better numerical methods for traversing these scales 
and designing, controlling and optimizing under uncertainty are also needed. And 
control techniques must make use of increased in situ measurements to control 
increasingly complex phenomena. 

Advances in materials and processing are important for a variety of industries 
in which control of complex process systems enables growth in the world economy. 
One example is the microelectronics industry, which has an average annual growth of 
20%, with sales of $200 billion in 2001. As described by the International Technology 
Roadmap for Semiconductors,^ high performance feedback control will be needed to 
achieve the small length scales required for the next generation of microelectronic 
devices that are predicted (and hence demanded) by Moore's Law. 

A second example is the pharmaceuticals industry, which is growing at 10-20% 

^http : //public . itrs . net 

3.5. Materials and Processing 


Figure 3.12. (a) Microscope image of paracetamol crystals (paracetamol 
is the active ingredient in Tylenol (h)), which shows the variability in crystal shape 
that can occur at a single time instance in a pharmaceutical crystallizer. Image 
courtesy of Richard Braatz. 

annually, with sales of $150 billion in 2000. The primary bottleneck to the opera- 
tion of production-scale drug manufacturing facilities is associated with difficulties 
in controlling the size and shape distribution of crystals produced by complex crys- 
tallization processes (see Figure 3.12). Crystallization processes typically involve 
growth, agglomeration, nucleation, and attrition mechanisms which can be affected 
by particle-particle collisions. Poor control of this crystal size distribution can com- 
pletely halt the production of pharmaceuticals, causing both economic and medical 

In addition to the continuing need to improve product quality, there are several 
other factors in the process control industry that are drivers for the use of control. 
Environmental regulations continue to place stricter limitations on the production of 
pollutants, forcing the use of sophisticated pollution control devices. Environmental 
safety considerations have led to the design of smaller storage capacities to diminish 
the risk of major chemical leakage, requiring tighter control on upstream processes 
and, in some cases, supply chains. Large increases in energy costs have encouraged 
engineers to design plants which are highly integrated, coupling many processes 
that used to operate independently. All of these trends increase the complexity of 
these processes and the performance requirements for the control systems, making 
the control system design increasingly challenging. 

As in many other application areas, new sensor technology is creating new 
opportunities for control. Online sensors — including laser backscattering, video mi- 
croscopy, ultraviolet, infrared, and Raman spectroscopy — are becoming more robust 
and less expensive, and are appearing in more manufacturing processes. Many of 
these sensors are already being used by current process control systems, but more 
sophisticated signal processing and control techniques are needed to more effectively 
use the real-time information provided by these sensors. Control engineers can also 
contribute to the design of even better sensors which are still needed, for example, 
in the microelectronics industry. As elsewhere, the challenge is making use of the 
large amounts of data provided by these new sensors in an effective manner. In 

68 Chapter 3. Applications, Opportunities, and Challenges 

addition, a control-oriented approach to modeling the essential physics of the un- 
derlying processes is required to understand fundamental limits on observability of 
the internal state through sensor data. 

Another common feature in materials and process control is the inherent com- 
plexity of the underlying physical processing. Modern process systems exhibit very 
complex nonlinear dynamics, including substantial model uncertainty, actuator and 
state constraints, and high dimensionality (usually infinite). These systems are 
often best described by tightly coupled systems of algebraic equations and stochastic 
partial integrodifFerential equations with widely varying time and length scales and 
significant nonlinearities. This is especially true in the microelectronics industry, 
where hundreds of stiff partial differential equations can be required for predicting 
product quality, for example, during the modeling of cluster formation and dis- 
solution during fast-ramp annealing after ion bombardment. Other processes are 
best described by kinetic Monte Carlo simulations , with or without coupling to 
continuum equations, which can be run on serial or parallel computers. Both iden- 
tification and control algorithms are needed that can simultaneously address the 
high complexity, high nonlinearity, and high dimensionality of these complex pro- 
cess systems. Furthermore, there is significant uncertainty associated with many of 
the kinetic parameters, even with improved sensors, so these algorithms need to be 
robust to model uncertainties. 

Two specific areas that illustrate some of the challenges and future needs are 
control of particulate systems and biotechnology. 

Control of Particulate Systems 

Particulate processes are prevalent in a number of process industries including agri- 
cultural, chemical, food, minerals, and pharmaceutical. By some estimates, 60% 
of the products in the chemical industry are manufactured as particulates with an 
additional 20% using powders as ingredients. One of the key attributes of such 
systems is the distributed characterization of physical and chemical properties such 
as size, shape, morphology, porosity, and molecular weight. The underlying mech- 
anisms which describe the evolution of such systems are captured by population 
balance models, which are coupled sets of hyperbolic partial differential and alge- 
braic equations. 

There are a number of challenges in the numerical solution of such equations, 
particularly when considering real-time applications such as model-based control. 
Critical in such models are the kernels or driving forces (e.g., nucleation, growth, 
agglomeration, and breakup) that are typically not well characterized, and are often 
determined from process data via various identification techniques. These prob- 
lems become increasingly complex as one considers higher-dimension population 
balance models (e.g., size and shape), where the number of parameters in the ker- 
nels grows rapidly with the increase in additional degrees of freedom. At the same 
time, there have been substantial advances in the domain of sensor technology, 
such that attributes like the particle size distribution can be measured in real-time 
by a variety of techniques including light scattering, ultrasound spectroscopy and 
hydrodynamic capillary separation. This leads to control formulations involving 

3.5. Materials and Processing 69 

distributed measurement variables, highly nonlinear process models, nonlinear op- 
erating constraints, and complex hierarchical operating objectives. 

To explore some of the major challenges, we consider three selected application 
areas — polymerization, granulation, and profile control. 

Emulsion Polymerization. Increasing global competition for the production of 
higher quality polymer products at lower costs, coupled with a general trend away 
from new capital investments in the U.S., has placed considerable pressure on the 
process engineers in the U.S. to operate the existing polymer plants more efficiently 
and to use the same plant for the production of many different polymer products. 
Lack of sufficient controllability is a barrier to better product quality control in 
some polymer processes. In many polymer processes, better product quality re- 
quires minimizing/maximizing several product quality indices simultaneously. This 
multi-objective requirement may result in narrow ranges of process trajectories, 
putting a premium on the controllability of the process. For instance, in coatings, 
the product's composition, molecular weight, and particle size distributions should 
be maintained simultaneously in limited ranges to ensure the coating has a desired 
level of film formation, film strength, and gloss. 

The critical link between these product quality indices and the operating pro- 
cess variables is often the distributed attribute such as the size distribution. In the 
past, such attributes were controlled indirectly using inferential control schemes, 
but online sensor technology brings the promise of real-time control of these prop- 
erties. This motivates the development of refined quantitative relationships between 
the distributed quantities and the quality variables. While experimental techniques 
have been used to develop relationships that hold for limited operating conditions, 
these descriptions do not readily lend themselves to optimization, either in terms 
of productivity or reduction in variance. 

Granulation. Granulation is a key step in many particulate processes where fine 
particles are agglomerated with the aid of a liquid binder into larger granules. It is 
often used to improve the visual appearance and/or taste of materials, improve the 
fiowability of the materials, enable compaction and tableting, and reduce dustiness. 
The granulation process exhibits many characteristics common to other particulate 
processes such as crystallization and emulsion polymerization. Typically, a desired 
product quality can be inferred from the Particle Size Distribution (PSD) of a pro- 
cess. The ability to manipulate a PSD allows for control of the end product quality, 
but PSD control can pose a very difficult control problem due to the significant 
multi-variable interacting character of PSD systems. In some situations, values of 
the measured PSD may be constrained to a specified acceptable region in order to 
achieve a desired product quality. 

As with many particulate processes, there is a rich interplay between mecha- 
nisms at the microscopic, mesoscopic and macroscopic levels in granulation, how- 
ever, the fundamental knowledge to link these mechanisms for use in model-based 
control is rather limited. In particular, the tradeoffs between model quality and 
complexity for various model uses have not been investigated systematically, lead- 
ing to inadequate selections of model forms. Furthermore, granulation is a complex 
multiscale process, including multi- number, dimension and time scales. The current 

70 Chapter 3. Applications, Opportunities, and Challenges 

status of granulation research clearly shows significant gaps between microscopic- 
level studies and plant-scale modeling, and also between the model forms and the 
use of models. Given such models and the already existing sensor technology, one 
can realize the tight regulation of this complex unit operation. 

Profile Control. Though the systems described in this area are not strictly partic- 
ulate processes, they share the attribute that a distributed variable is directly tied 
to product performance, hence many of the underlying mathematical constructs 
required for control are common to both classes of problems. The problems of 
controlling a "profile" arise in a number of rather different process industry unit 
operations, including polymer extrusion, cross direction control (paper , aluminum, 
glass, etc.), tubular chemical reactors, and advanced materials processing (photo- 
voltaic, microelectronic, etc.), to name a few. In some instances the properties of 
interest are measured in the cross direction (CD) giving rise to a 1-D profile control 
problem, or in other cases the quality attribute is measured in both the machine 
direction (MD) and CD, giving rise to a 2-D sheet control problem. In reaction 
unit operations, the extent of reaction across a spatial direction is a critical param- 
eter that controls important quality indices. For example, in a pulp digester, the 
control of reaction extent profile (measured by the Kappa number) along the axial 
direction in the reactor enables the tight regulation of critical fiber properties, such 
as strength, which depend on the reaction path as well as the final conversion. 

One of the interesting challenges that arises, for example, in the paper machine 
CD control problem is that hundreds of input/output variables are involved, com- 
plex temporal and spatial constraints must be maintained, and real-time require- 
ments dictate solution times on the order of seconds. This is further complicated by 
non-ideal process behavior owing to paper shrinkage, lane shifting, production grade 
and rate changes — all of which give rise to significant plant-model mismatch, and 
hence a robust controller is required. As with the particulate problems, the sensor 
technology is changing rapidly, enabling richer formulations of controlled operation. 


While process control has played a relatively minor role in the biotechnology in- 
dustries in past years, its value as an enabling technology is increasing, owing to 
novel developments in sensor technology coupled with advances in the mathemati- 
cal characterization of intracellular behavior. Furthermore, the potential to realize 
efficient process design by accounting for operational issues (i.e., combined design 
and control), brings promise of reducing the development time for new plants, and 
maximizing the production interval in a patent window. 

Classical bioreactor control focused on the manipulation of residence time, 
nutrient feed and the reactor environment (agitation, temperature, pH, etc.) in 
response to averaged system measurements (dissolved oxygen, temperature, pH, 
limited metabolite concentrations, etc.) Advances in sensor technology have en- 
abled direct measurement and manipulation of intracellular mechanisms, and recent 
advances in quantitative modeling of bioprocesses allow a much more thorough un- 
derstanding of the underlying biochemical processes. A number of the resulting 

3.5. Materials and Processing 71 

model structures are inspired from systems engineering concepts, such as the cyber- 
netic model which incorporates optimal control regulation of cellular processes, or 
the flux balance analysis which accounts for the convex constraint space available 
from a metabolic network. Population balance models also find application in this 
area, for example, in the description of age distributions in oscillating microbial cell 
cultures. As with particulate systems, one can construct high-order population bal- 
ance descriptions by accounting for the various elements of the physiological state 
space (DNA, RNA, protein, etc.) Commensurate with this increase in structural 
complexity is the possibility to achieve refined control objectives for bioprocessing, 
such as the control of distinct metabolic pathways. 

An example of the opportunities that emerge from such increased understand- 
ing is the use of recombinant organizations to produce enzymes and proteins. Typ- 
ically, the genes corresponding to the desired product are inserted into the microor- 
ganism through a plasmid. The first phase in the recombinant protein production 
involves increasing the cell productivity (biomass concentration), as the expression 
of the foreign protein imposes additional metabolic burden on the organism and de- 
creases the growth rate. Once a sufficiently high biomass concentration is achieved, 
the inducer that expresses the inserted gene is added in the second phase resulting 
in the synthesis of the recombinant product. Therefore, the concentration of the 
inducer and the time at which the inducer is added are key variables in maximizing 
the amount of the recombinant protein formed. One specific example is the recom- 
binant bioprocess involving chloramphenicol acetyltransferase (CAT) production in 
the genetically engineered strain of E. coli JM105. This strain produces both the 
green fluorescent protein (GFP) and CAT, when the metabolized inducer, arabi- 
nose, is added to the bioreactor. The objective is the maximization of the amount 
of CAT formed at the end of a batch. The manipulated variables are the glucose 
feed rate and the feed rate of arabinose, the inducer which turns on the expression 
of the desired product. 

The use of GFP and its variants have revolutionized experimental biology 
by enabling accurate real-time reporting of intracellular location and interactions, 
which has proven valuable in determining the role and interactions of a number of 
proteins. GFP was cloned from the jellyfish, Aequorea victoria, in 1992 and has 
since been successfully expressed in a number of host organisms as a reporter of 
expression, as well as to identify the location of proteins within cells. GFP and its 
variants have been successfully used to quantitate intracellular expression levels in 
insect larvae, bacterial systems and mammalian cells. Owing to the optical nature 
of the signal, the development of sensing devices for industrial application is direct. 

72 Chapter 3. Applications, Opportunities, and Challenges 

3.6 Other Applications 

The previous sections have described some of the major apphcation areas discussed 
by the Panel. However, there are certainly many more areas where ideas from 
control are being applied or could be applied. In this section we collect a few such 
areas which are more specialized than those discussed to this point. As before, these 
areas are not meant to be exhaustive, but rather representative of some of the new 
and exciting directions within control. 

Environmental Science and Engineering^ 

It is now indisputable that human activities have altered the environment on a global 
scale. Problems of enormous complexity challenge researchers in this area and first 
among these is to understand the feedback systems that operate on the global scale. 
One of the challenges in developing such an understanding is the multiscale nature of 
the problem, with detailed understanding of the dynamics of microscale phenomena 
such as microbiological organisms being a necessary component of understanding 
global phenomena, such as the carbon cycle. Two specific areas where control is 
relevant are atmospheric systems and microbiological ecosystems. 

Atmospheric systems and pollution. Within the last few years "inverse modeling" 
has become an important technique in atmospheric science when there are unknown 
sources or sinks of a species. The essential problem is to infer an optimal global 
source (or sink) distribution of an atmospheric trace species from a set of global 
observations. This is equivalent to the following control problem: given a system 
governed by a set of partial di0"erential equations (PDEs) and a set of noisy obser- 
vations of the system, determine the optimal set of inputs that match the model to 
the data. Such a problem has relevance to atmospheric chemical transport models, 
of which CO2 is perhaps the most important at the present time. 

At present, inverse modeling for atmospheric species has been applied only to 
those compounds that are inert in the atmosphere or only react via simple mech- 
anisms. One area that offers promise is the development of techniques for inverse 
modeling to trace species that undergo nonlinear atmospheric processes, such as 
ozone. The inverse modeling problem is closely related theoretically to the sensitiv- 
ity analysis problem, wherein one seeks the sensitivity of spatially and temporally 
varying concentrations to uncertainties in input functions and variables. Atmo- 
spheric inverse modeling is an important application of ideas from control to esti- 
mate global source (and sink) distributions of trace species based on noisy, usually 
sparse measurements. 

Microbiological ecosystems. To illustrate how ideas from control can play a role in 
microbiological ecosystems, consider the example of microbial biofilms. It is widely 
recognized that microbial biofilms are ubiquitous, resilient, responsive to their en- 
vironment, and able to communicate through chemical signaling. Furthermore, 
specific genes, gene products, and regulatory networks that control how bacteria 

' The Panel would like to thank Jared Leadbettor, Diannc Newman, and John Seinfeld for their 
contributions to this section. 

3.6. Other Applications 73 

coniniunicate have been described in a variety of bacteria. To date, studies of 
biofilni development have been largely limited to studies of pure cultures. While 
much has been learned regarding the genetic pathways taken by a variety of or- 
ganisms when transitioning from the planktonic to the sessile phase, little is known 
about how these pathways change in response to changes in the environment. Re- 
searchers now believe that at the scale most relevant to bacteria (the microscale), 
one of the most important environmental factors that affect biofilm development 
by a given species is the presence of other organisms. The study of such ecological 
networks is at the forefront of research in this area and the tools of control can play 
a major role developing systematic understanding of their complex interactions. 

Another example is in the area of bacterial cells that live inside organisms. 
Although they have limited conventional sensing and decision making abilities, bac- 
terial cells are able to rapidly assess and respond to changes in their metabolism by 
monitoring and maintaining relative pool sizes of an extraordinary number (thou- 
sands) of cellular building blocks/intermediates. A common theme that has emerged 
in understanding how this works is related to timing. Many changes in physiology 
are effected by responses to pauses brought about by a binding site of an enzyme not 
being occupied by a given building block. If a certain building block is depleted, the 
enzyme that would incorporate it into cellular material pauses "in wait." Paused 
enzymes will often do or allow things that an occupied one does not. On one hand, 
this might result in the increased production of the missing metabolite to bring 
up the depleted pool to better reflect the size of the other building block pools, 
keeping things in balance. On the other, some enzymes pause as a result of many 
pools being depleted in concert, this signals to the cell that it has begun to exhaust 
its total resources and moves it into a starvation survival phase. 

To control for the possible overproduction of certain pools, many enzymes 
involved in the early stages of building block synthesis become inactive if a binding 
site becomes occupied by a later or final product. With the knowledge of this control 
mechanism, industrial microbiologists have been able to obtain feedback inhibition 
mutant bacteria that over produce almost any desired amino acid. 

In the natural world, work done on termites provides a model system for 
studying the role of feedback and control in such microbiological ecosystems. There 
is every reason to believe that termites can control the delivery of oxygen to, and 
the consumption of it within differing zones of the gut epithelium. By doing so, the 
termite should be able to protect and even control the activities of its oxygen sen- 
sitive microbiota — but the forms of feedback that the tissue receives and processes 
from the gut and atmosphere are not known. One could envision several ways in 
which the gut tissue might respond to oxygen and acetate concentrations to control 
oxygen delivery to, and diffusion into the gut compartment. An important question, 
and one which control can help provide an answer, is how the insect and gut tissues 
create, control, and maintain a very complex and fragile ecosystem. 

74 Chapter 3. Applications, Opportunities, and Challenges 

Economics and Finance^ 

Many control tools have found applications in economics and there are many com- 
mon mathematical methods between the fields in areas such as game theory, stochas- 
tic modeling and control, and optimization and optimal control. 

Control theory also became an important tool in finance with the introduction 
of the Black-Scholes-Merton formula for pricing options in the early 1970s. In 
essence, they showed that the dynamic control of a portfolio of risky assets could 
be used to change its risk profile. In the extreme case of options, the risk could be 
completely eliminated by a dynamic trading strategy, and this led to a fair price for 
the option. 

The general problem of pricing and hedging an option is one of optimal 
stochastic control, and involves dynamically trading financial assets to achieve de- 
sired payoffs or risk profiles. When placed in this control theory framework, the 
quantities of various assets held in a portfolio become the decision variables, the 
price movements (often random) of the assets are the dynamics of the system, and 
achieving a desired risk profile is the objective. In structure, they tend to deviate 
from control problems involving physical systems due to the fact that the dynamics 
of the system are dominated by uncertainty. That is, the movement of prices is 
modeled in a highly stochastic manner. 

Control problems in finance, especially those related to hedging and pricing 
derivative securities, present a number of interesting challenges for the operations 
research and control communities. 

The securities being offered in the financial marketplace are becoming increas- 
ingly complex. That means that the pricing and hedging of these securities is also 
becoming increasingly complex. Examples already in existence include options that 
allow the holder to decide when to exercise the option, options on averages of prices, 
options on baskets of securities, options on options, etc. and these options can be 
written on stocks, futures, interest rates, the weather, earthquakes and catastro- 
phes, etc. Hedging of these options is a challenging and rather daunting task for 
stochastic control theory. 

The lack of robustness of dynamic schemes in use during the 1987 crash was 
another critical factor. Since modeling is itself a difficult problem, it is important 
that control schemes work well in the presence of modeling errors. This is especially 
true in finance, where uncertainties can be large and time varying. Often this 
uncertainty is handled in a stochastic manner. For instance, some models in finance 
assume that the volatility of an asset is stochastic. This has been used to explain 
certain deviations between markets and theory. More recently, researchers have 
been developing control and hedging schemes which explicitly account for model 
errors and uncertainty, and are designed to work well in their presence. This will 
be an area in which robust control theory has a great amount to contribute. 

'The Panel would like to thank Jim Primbs for his contributions to this section. 

3.6. Other Applications 75 


The development of adaptive optics and phased array antennas have created new 
opportunities for active wavefront control in a variety of applications. Cancellation 
of atmospheric effects is already being used in scientific and military applications 
and more sophisticated uses of control are now being considered. One potential area 
for new work is in the area of active electromagnetic nulling for stealth applications. 

To avoid detection and targeting, great strides have been achieved in reducing 
the radar cross section of military systems . Perhaps the best known advance has 
been the use of angularity and radar absorbing materials to minimize the detection 
of fighter aircraft. The narrow forward profile of the stealth fighter is very effective in 
minimizing radar refiection. However, there are many limitations of this approach. 
Radar cross section increases whenever the pilot performs a banking turn and radar 
absorbing materials used are expensive and susceptible to moisture. Furthermore, 
multistatic radar systems, which can increasingly be built inexpensively, effectively 
track a stealth fighter, and engine exhaust infrared signatures represent serious 
system vulnerabilities. 

Rather than use angularity to deflect incoming tracking or targeting radiation, 
a different approach is to develop inexpensive antenna arrays that will actively null 
incoming radiation. The use of ferrite structures in antennas could allow extremely 
rapid change of their radiating and receive properties. This would in turn allow 
arrays of such antennas to be used to intelligently respond to the surrounding elec- 
tromagnetic environment by increasing the self- absorption of impinging radiation 
and by in turn radiating a fleld that will null further incoming radiation. 

Several challenges must be overcome to achieve this goal, including distributed 
control system theory to define the currents applied to the radiating antenna to null 
the incoming radiation. The problem of field sensing and prediction in order to con- 
trol its subsequent evolution is a significant mathematical and electrical engineering 
challenge. Advances in this area could have other applications in cellular phone com- 
munications systems, adaptive multistatic radar systems, and other directed energy 

Infrared exhaust signatures are another possible application area for active 
nulling. The complex flame dynamics in a gas turbine engine result in a statisti- 
cally rich infrared field. A fundamental question is whether a finite array of infrared 
sensors and laser diodes could be used to sense, characterize, and control this elec- 
tromagnetic structure. 

Molecular, Quantum and Nanoscale Systems 

Control of molecular, quantum and nanoscale systems is receiving increased atten- 
tion as our ability to sense and actuate systems at this scale improves. Recent 
progress in computational chemistry and physics has enabled the predictive sim- 
ulation of nanoscale materials behavior and processing, in systems ranging from 
nanoparticles to semiconductor heterostructures to nanostructured bulk materials. 
With this physical understanding and a mathematical model, it is now possible for 
formulate optimization and control questions for nanoscale materials and systems. 

76 Chapter 3. Applications, Opportunities, and Challenges 

Applications include design of nanostructured materials, precision measurement, 
and quantum information processing. 

Macroscopic materials are well-described by their bulk properties, but as a 
structure's size shrinks to nanometers, bulk descriptions no longer capture the rele- 
vant physics. Surface effects become increasingly important and alter the electronic 
properties. These new properties may be exploited in a variety of engineering 
applications, from quantum dot lasers to ultra-hard coatings. A major challenge 
in exploiting these unique features is the ability to manufacture materials at the 
nanometer scale using high-throughput manufacturing processes. Improved first- 
principles models, new techniques for data rich sensing and in-situ diagnostics, 
design of new actuation approaches, and algorithms for controlling microscale phe- 
nomena are required and the control community can be a major contributer to 
progress in this area. 

There are many open questions in the control of phenomena at this scale. 
Brown and Rabitz [12] divide these into three categories: control law design, closed 
loop implementation, and identification of the system Hamiltonian. New results in 
controllability, optimal control theory, adaptation and learning, and system identifi- 
cation are required to make progress in this area. What makes the problem difficult 
is the use of quantum wave interference as a mechanism for achieving prescribed 
control objectives such as the selective dissociation of a polyatomic molecule or 
the manipulation of wavepackets in semiconductors. Recent experimental successes 
(see [12] for more details) include cleaving and rearranging selected chemical bonds, 
control of fluorescence in polyatomic molecules and enhanced radiative emission in 
high harmonic generation. 

Control of quantum systems also provides a new set of tools for understanding 
nature, as described in the vignette on quantum measurement and control (page 21). 

Energy Systems 

Control has always been a central element in the design of large scale energy systems. 
From their origins as single generators connected to associated loads, power systems 
began around 70 years ago to evolve into more broadly interconnected systems, 
motivated among other things by reliability (loads are now not totally dependent 
on any particular generator). Recent outage events have highlighted, however, that 
reliability is a more subtle question, and in fact global connectivity can lead to the 
multiplication of failures. 

At same time, the industry is currently undergoing deregulation, which could 
easily lead to a loosening of control and a shortage of system information (even 
about neighbors), elements which are key to the successful containment of failures. 
There is a significant risk that without a technological effort to improve the re- 
liability of the network against failure, we can expect increased vulnerability of 
this fundamental infrastructure. One aspect of this effort concerns the design and 
management policies for the grid. This includes network expansions, or the deploy- 
ment of new technological capabilities such as Flexible AC Transmission Systems 
(FACTS), and the decisions on load distribution to service the required demand. 

Another area where fundamental research can have significant impact is real- 

3.6. Other Applications 77 

time control for correct dynamic regulation and event-management for the contain- 
ment of failures. There are increased linkages between computer and communi- 
cations systems with the electric power grid, including the Internet and commu- 
nications by the utilities to the in-orbit satellite network used for the Wide Area 
Measurement System (WAMS). This increased connectivity allows the possibility 
for significant local processing power at each generator, connected to a global data 
network providing information about the current state of the grid. 

The technological challenges posed by such a system are multiple. A first 
challenge, even assuming a free and instantaneous flow of information, is to de- 
velop simulation capabilities for analysis of faults with the potential for large-scale 
cascading failures. Note that after a major outage it is possible to go back over 
the data and obtain a simple explanation. However, going beyond post mortems 
and into prediction will require truly revolutionary advances in basic simulation 
capability, indeed a completely new way of doing simulations of complex systems, 
going beyond both the traditional top-down approach that has dominated scientific 
computing, as well as the more recent bottom-up style of agent-based methods. 

Secondly, distributed software protocols must be implemented for event-flow 
management and distributed simulation. A great challenge in this regard is that 
both the autonomy and the economic interests of the individual power companies 
must be protected. In other words, the distributed software system must be struc- 
tured so that clear boundaries are drawn between serving the common good and 
protecting proprietary and economic information. Finally, note that with this net- 
work we are actually increasing inter connectedness, a driving factor of cascading 
failures. One should be wary of this process, which could lead to failures propa- 
gating through both networks. In particular, software systems should incorporate 
robustness against these kinds of events. 

Progress in this area will require research progress in control theory and dy- 
namical systems, along with the insight of statistical physics and new results in 
numerical methods, distributed software, computer networks and power systems. 
Each of these areas is relevant, and experts in each domain must expand their vi- 
sion to tie to the other areas. As we evolve toward a "mega-infrastructure" that 
includes energy (including the electric grid, water, oil and gas pipelines), telecom- 
munications, transportation, and electronic commerce, concepts from control will 
become even more important and will present significant challenges to the research 

There are also many applications in energy systems that fall under more tradi- 
tional paradigms, such as advanced control of gas turbines for power generation and 
fuel cells. Both represent complex chemical and fluid systems that must operate 
in a variety of environmental conditions with high reliability and efficiency. Fuel 
cells present particularly difficult challenges due to the tightly coupled nature of 
the underlying thermal, fluid, and chemical processes. In mobile applications, such 
as automobiles, control is important for quickly bringing the fuel cell to the desired 
operating point and maintaining the operation of the cell under changing loads. 

78 Chapter 3. Applications, Opportunities, and Challenges 

Chapter 4 

Education and Outreach 

Control education is an integral part of the community's activities and one of its 
most important mechanisms for transition and impact. In 1998, the National Sci- 
ence Foundation (NSF) and the IEEE Control Systems Society (CSS) jointly spon- 
sored a workshop in control engineering education which made a number of recom- 
mendations for improving control education (see [1] and Appendix A). This section 
is based on the findings and recommendations of that report, and on discussions 
between Panel members and the control community. The Panel would particularly 
like to thank Jim Batterson for his contributions to this chapter. 

4.1 The New Environment for Control Education 

Control is traditionally taught within the various engineering disciplines that make 
use of its tools, allowing a tight coupling between the methods of control and their 
applications in a given domain. It is also taught almost exclusively within engi- 
neering departments, especially at the undergraduate level. Graduate courses are 
often shared between various departments and in some places are part of the cur- 
riculum in applied mathematics or operations research (particularly in regards to 
optimal control and stochastic systems) . This approach has served the field well for 
many decades and has trained an exceptional community of control practitioners 
and researchers. 

Increasingly, the modern control engineer is put in the role of being a systems 
engineer, responsible for linking together the many elements of a complex product 
or system. This requires not only a solid grounding in the framework and tools 
of control, but also the ability to understand the technical details of a wide vari- 
ety of disciplines, including physics, chemistry, electronics, computer science, and 
operations research. 

In addition, control is increasingly being applied outside of its traditional 
domains in aeronautics, chemical engineering, electrical engineering and mechanical 
engineering. Biologists are using ideas from control to model and analyze cells 
and animals; computer scientists are applying control to the design of routers and 


80 Chapter 4. Education and Outreach 

embedded software; physicists are using control to measure and modify the behavior 
of quantum systems; and economists are exploring the applications of feedback to 
markets and commerce. 

This change in the use of control presents a challenge to the community. In 
the United States, discipline boundaries within educational institutions are very 
strong and it is difficult to maintain a strong linkage between control educators and 
researchers across these boundaries. While the control community is large and pros- 
perous, control is typically a small part of any given discussion on curriculum since 
these occur within the departments. Hence it is difficult to get the resources needed 
to make major changes in the control curriculum. In addition, many of the new 
applications of control are outside of the traditional disciplines that teach control 
and it is hard to justify developing courses that would appeal to this broader com- 
munity and integrate those new courses into the curricula of those other disciplines 
(e.g., biology, physics, or medicine). 

In order for the opportunities described elsewhere in this report to be realized, 
control education must be restructured to operate in this new environment. Several 
universities have begun to make changes in the way that control is taught and 
organized and these efforts provide some insights into how this restructuring might 
be done successfully. 

Often the first step is establishing a cross-disciplinary research center, where 
there is a larger critical mass of control researchers. Examples include the Coordi- 
nated Science Laboratory (CSL) at the University of Illinois, Urbana-Champaign, 
the Center for Control Engineering and Computation (CCEC) at the University 
of California, Santa Barbara, and the Institute of Systems Research (ISR) at the 
University of Maryland. These centers coordinate research activities, organize work- 
shops and seminars, and provide mechanisms for continuing interactions between 
control students and faculty in different departments. 

A second step is the establishment of shared courses between the disciplines, 
often at the graduate level. These shared courses encourage a broader view of 
control since the students come from varying backgrounds. They also provide an 
opportunity for the larger control community at the university to establish active 
dialogs and provide a mechanism for sharing students and building joint research 
activities. Many U.S. universities have adopted this model, especially for theory 
oriented courses. 

Finally, some schools have established a separate M.S. or Ph.D. program in 
control. These are common in Europe, but have been much less prevalent in the 
United States, partly due to the traditional discipline structure around which most 
universities are organized. Examples in the U.S. include the Control and Dynamical 
Systems (CDS) program at Caltech and the Department of Systems Science and 
Mathematics (SSM) at Washington University. The advantage of a separate gradu- 
ate program in control is that it gives the faculty better control over the curriculum 
and allows a less discipline-centric approach to control. 

One other mechanism, popular in Europe but not yet established in the United 
States, is the creation of regional control alliances that build critical mass by linking 
together multiple universities in a geographic region. This mechanism is used very 
effectively, for example, in the Netherlands through the Dutch Institute of Systems 

4.2. Making Control More Accessible 81 

and Control (DISC).^ With the increased availability of real-time audio, video, 
and digital connectivity, it is even possible to create virtual alliances — with shared 
classes, reading groups, and seminars on specialized topics — linking sites that are 
not physically near each other. 

4.2 Making Control More Accessible 

Coupled with this new environment for control education is the clear need to make 
the basic principles of feedback and control known to a wider community. As the 
main recommendations of the Panel illustrate, many of the future opportunities 
for control are in new domains and the community must develop the educational 
programs required to train the next generation of researchers who will address these 

A key element is developing new books and courses that emphasize feedback 
concepts and the requisite mathematics, without requiring that students come from 
a traditional engineering background. As more students in biology, computer sci- 
ence, environmental science , physics, and other disciplines seek to learn and apply 
the methods of control, the control community must explore new ways of provid- 
ing the background necessary to understand the basic concepts and apply some of 
the advanced tools that are available. Textbooks that are aimed at this more gen- 
eral audience could be developed and used in courses that target first year biology 
or computer science graduate students, who may have very little background in 
continuous mathematics beyond a sophomore course in scalar ordinary differential 
equations (ODEs) and linear algebra. 

The following vignette describes one attempt to make control more accessible 
to a broader community of research scientists and engineers. 

Vignette: CDS 110: Introduction to Control Concepts, Tools, and The- 
ory (Kristi Morgansen and Richard Murray, Caltech) 

The Control and Dynamical Systems Department at Caltech has recently undertaken 
a revision of its entry level graduate courses in control to make them accessible to 
students who do not have a traditional background in chemical, mechanical, or electrical 
engineering. The current course, CDS 110, is taken by senior undergraduates and first 
year graduate students from all areas of engineering, but has traditionally not been easily 
accessible to students in scientific disciplines, due to its heavy engineering slant. With 
the increased interest in control from these communities, it was decided to revise the 
course so that it could not only continue to serve its traditional role, but also provide an 
introduction to control concepts for first year graduate students in biology, computer 
science, environmental engineering, and physics. 

The goal of the course is to provide an understanding of the principles of feedback and 
their use as a tool for altering the dynamics of systems and managing uncertainty. 
The main topics of the course are modeling, dynamics, interconnection, and robustness 
of feedback systems. On completion of the course, students are able to construct 

^ http : //www . disc . tudelf t . nl 

82 Chapter 4. Education and Outreach 

control-oriented models for typical systems found in engineering and the sciences, specify 
and describe performance for feedback systems, and analyze open loop and feedback 
behavior of such systems. Central themes throughout the course include input/output 
response, modeling and model reduction, linear versus nonlinear models, and local versus 
global behavior. 

The updated version of the course has two "tracks" : a conceptual track and an analytical 
track. The conceptual track is geared toward students who want a basic understanding 
of feedback systems and the computational tools available for modeling, analyzing, and 
designing feedback systems. The analytical track is geared toward a more traditional 
engineering approach to the subject, including the use of tools from linear algebra, 
complex variables, and ordinary differential equations (ODEs). Both tracks share the 
same lectures, but the supplemental reading and homework sets differ. 

In addition to the main lectures, optional lectures are given by faculty from other disci- 
plines whose research interests include control. Hideo Mabuchi (Physics) and Michael 
Dickinson (Biology) are two such lecturers and they provide examples of some applica- 
tions of feedback to a variety of scientific and engineering applications. These lectures 
are used to emphasize how the concepts and tools are applied to real examples, drawn 
from areas such as aerospace, robotics, communications, physics, biology, and computer 

The first iteration of the course, taught in 2001-02, succeeded in developing a set of 
conceptual lectures (given as the first lecture in the week) that introduced the main ideas 
of control with minimal mathematical background. Based on these lectures, students 
are able to use the tools of control (e.g., MATLAB and SIMULINK) and understand the 
results. Two additional lecture hours per week are used to provide the more traditional 
mathematical underpinnings of the subject and to derive the various results that are 
presented in the conceptual lectures. 

In the second iteration of the course, to be taught in 2002-03, we intend to refine 
the lectures and put more effort into dividing the class into sections based on research 
interests. Individual lectures in the sections will then be used to build the necessary 
background (for example, providing a refresher on linear algebra and ODEs for biologists 
and computer scientists) or to provide additional perspectives (for example, linking 
transfer functions to Laplace transforms in a more formal manner). 

In addition to changes in specific courses on control, universities could also 
integrate modules on dynamics and control into their undergraduate mathemat- 
ics and science curricula. Any linear algebra course could be strengthened by the 
addition of a short lesson on linear systems, eigenvalues, and their physical interpre- 
tation and manipulation through feedback. Freshman physics could be enriched by 
extending lessons on mechanical oscillators to the problem of balancing an inverted 
pendulum or the stability of person riding a bicycle. 

The control community also must continue to implement its tools in software, 
so that they are accessible to users of control technology. While this has already 
occurred in some areas of control (such as classical and modern linear control the- 
ory) , there are very few general purpose software packages available for analysis and 

4.3. Broadening Control Education 83 

design of nonlinear, adaptive, and hybrid systems — and many of these are not avail- 
able on general pm'pose platforms (such as MATLAB). These tools can be used to 
allow non-experts to apply the most advanced techniques in the field without requir- 
ing that they first obtain a Ph.D. in control. Coupled with modeling and simulation 
tools, such as SIMULINK and Modelica, these packages will be particularly useful 
in teaching the principles of feedback by allowing exploration of relevant concepts 
in a variety of domains. 

4.3 Broadening Control Education 

In addition to changes in the curriculum designed to broaden the accessibility of con- 
trol, it is important that control students also have a broader grasp of engineering, 
science, and mathematics. Modern control involves the development and imple- 
mentation of a wide variety of very complex engineering systems and the control 
community has been a major source of training for people who embrace a systems 
perspective. The curriculum in control needs to reflect this role and provide stu- 
dents with the opportunity to develop the skills necessary for modern engineering 
and research activities. 

At the same time, the volume of work in control is enormous and so effort 
must be placed on unifying the existing knowledge base into a more compact form. 
There is a need for new books that systematically introduce a wide range of control 
techniques in an effective manner. This will be a major undertaking, but is required 
if future students of control are to receive a concise but thorough grounding in the 
fundamental principles underlying control, so that they can continue to extend the 
research frontier beyond its current boundary. 

Increasingly, control engineers are playing the role of "systems integrator" in 
large engineering projects. This occurs in part because they bring systems insight 
that is required for successful operation of a complex engineering product, but also 
because control is often the glue that ties together the components of the system 
(often in the form of embedded control software). Unfortunately, most control 
curricula do not emphasize the types of leadership and communications skills that 
are critical for success in these environments. 

A related aspect of this is strengthening the skills required for working in 
teams. All modern systems design is done in interdisciplinary teams and it requires 
certain skills to understand how to effectively interact with domain experts from a 
wide variety of disciplines. Project courses are an effective mechanism for developing 
this type of insight and these should be more aggressively incorporated into control 
curricula at both undergraduate and graduate levels. Another effective mechanism 
is participation in national competitions where control tools are required, such as 
RoboCup2 and FIRST^. 

It is also important that control students be provided with a balance between 
theory, applications, and computation. In particularly, it is essential that control 
students build a deep domain knowledge in one or more disciplines, so that they un- 

"http: //www.robocup. org 
http; //www.usf irst . org 

Chapter 4. Education and Outreach 

derstand how this knowledge interacts with the control methodology. Independent 
of the specific domain chosen, this approach provides a context for understanding 
other engineering domains and developing control practices and tools that bridge 
application areas. 

Experiments continue to form an important part of a control education and 
projects should form an integral part of the curriculum for both undergraduate and 
graduate students. Shared laboratories within individual colleges or universities as 
well as laboratories shared among different universities could be used to implement 
this (with additional benefits in building cross-disciplinary and cross-university in- 
teractions). New experiments should be developed that explore the future frontiers 
of control, including increased use of computing, communications and networking, 
as well as exploration of control in novel application domains. 

4.4 The Opportunities in K-12 Math and Science 

Much as computer literacy has become commonplace in our K-12 curriculum, an 
understanding of the requirements, limits, and capabilities of control should become 
part of every scientifically literate citizen's knowledge. Whether it is understanding 
why you should not pump antilock brakes or why you need to complete a regimen of 
antibiotics through the final pills even after symptoms disappear, an understanding 
of dynamics and control is essential. The development of inexpensive microproces- 
sors, high-level computer languages, and graphical user interaces (GUIs) has made 
the development of test apparatus and small laboratories for rudimentary control 
experiments and demonstrations available within the budgets of all school districts. 
The U.S. National Science Foundation recognizes the importance of its funded pro- 
grams impacting the general public through its "Criterion 2" (Broader Impacts) 
in the evaluation of all submitted proposals. Because of the broad applications of 
control to the public good and standards of living, it is important to develop a 
curriculum for inclusion in pre-college (K-12) education. 

Currently, mathematics, science, and computer technology are taught in sep- 
arate departments in the vast majority of K-12 curricula. Even sciences are com- 
partmentalized at many schools. As at universities, the multidisciplinary nature 
of control is very much antithetical to that traditional thinking and structure in 
K-12 education. However, there is some evidence of advances toward application 
and integration of mathematics with science. The Consortium for Mathematics 
and Its Applications (COMAP)^, which develops curriculum materials and teacher 
development programs in mathematics, is one example. Indeed, the leveraging of 
efforts with COMAP could prove fruitful and the control community could work 
with COMAP to enhance the current textbooks and curricula that have been de- 
veloped by that consortium over the past two decades. Another resource is the 
Eisenhower National Clearinghouse,^ which maintains a database of teaching mod- 
ules and resources for K-12 math and science education. 

http://www. comap. com 
http : //www . enc . org 

4.5. Other Opportunities and Trends 85 

In the control arena, simple experiments involving governors, thermostats, and 
"see-saws" can be performed as early as elementary school to illustrate the basic 
concepts of control. As mathematical sophistication increases through middle school 
and high school, quantitative analysis can be added and experimentally verified. 
Some schools are beginning to teach calculus in the junior year and so a post-calculus 
course in applied mathematics of differential equations and dynamical systems could 
be created bridging chemistry, physics, biology, and mathematics. 

Complementary to the development of educational materials and experiments, 
it is also important to provide K-12 teachers with the opportunities to learn more 
about control. As an example of how this could be done, NASA Langley Research 
Center sponsored a program for teachers under the auspices of the HPCCP (High 
Performance Computing and Communications Program) several years ago. In this 
program teachers from six school districts spent 8 weeks learning the state of the 
art in computer hardware and software for engineering and science. Most days were 
spent with new material delivered in a lecture or laboratory environment in the 
morning with a "homework" laboratory in the afternoons. Teachers were paid a 
fellowship that approximated the per diem rate of entry-level teachers. This type of 
residential environment allowed for a total immersion in the material. In addition 
to becoming familiar with research- grade hardware and software and the Internet, 
the participants formed partnerships with each other that promoted continued col- 
laboration throughout the coming academic years. 

There are numerous curriculum development and general education meetings 
and conferences throughout the country each year. In particular, most states have 
an active association of school boards and there is a National School Boards As- 
sociation. A presentation at these meetings would communicate directly with the 
policy and decision makers. Such a presentation would have to be tailored for the 
lay person but might produce a pull to match a push from one of the ideas above. 

4.5 Other Opportunities and Trends 

In addition to the specific opportunities for education and outreach described above, 
there are many other possible mechanisms to help expand the understanding and 
use of control tools. 

Popular Books and Articles 

In September 1952, Scientific American published an entire issue dedicated to Au- 
tomatic Control [39]. The issue highlighted the role that control was playing in 
the new advancements of the time, particularly in manufacturing. The introduc- 
tion of cruise control (originally called Autopilot) a few years later provided direct 
experience with the main principles of feedback. 

Since that time, control has become less and less visible to the general public , 
perhaps in part because of its success. Individuals interact with control systems and 
feedback many times every day, from the electronic amplifiers, tuners, and filters in 
television and radio, to congestion control algorithms that enable smooth Internet 
communications, to flight control systems for commercial aircraft. Yet most people 

86 Chapter 4. Education and Outreach 

are unaware of control as a discipline. Other fields, such as artificial intelligence, 
robotics, and computer science have often been given credit for ideas whose origins 
lie within the control community. 

There is a great need to better educate the public on the successes and oppor- 
tunities for control. This public awareness is increasingly important in the face of 
decisions that will need to be made by government funding agencies about support 
for specific areas of research. 

The use of any number of popular outlets for communication can reach this 
group. Many local newspapers now have a science page or section on a weekly basis. 
The development of a popular level series of articles on dynamics and control could 
be prepared for these pages. The New York Times publishes a science section every 
Tuesday; a series of articles could be developed for this section spanning several 
weeks. A number of science museums have been developed across the nation in 
recent years and many of these museums are allied through professional associations. 
The development of interactive dynamics and control displays for these museums 
would be beneficial to the museum by giving them a new exhibit and the displays 
reach the entire age range of the public from children through adults. 

Books written for non-specialized audiences and chapters in high school text- 
books are another mechanism for increasing the understanding of control principles 
in the general population. The dynamical systems community has been very suc- 
cessful in this regard, with many books available on chaos, complexity theory, and 
related concepts. Currently available books on control include books on the history 
of control [8, 9, 27] and a book entitled "Out of Control" [22] that discusses many 
control concepts. 

Multimedia Tools 

There is an increasing need for educational materials that can be used in a variety of 
contexts for communicating the basic ideas behind control. One possible mechanism 
is to develop a multimedia CDROM that would include materials on the history 
and concepts of control, as well as tutorial material on specific topics and public 
domain software tools for control analysis and design. 

The fluid mechanics community has recently developed such a multimedia 
CDROM that can be used as a supplement to traditional courses in fluid mechan- 
ics [18]. It contains historical accounts of fluid mechanics, videos and animations of 
important concepts in fluids, and detailed descriptions of fundamental phenomena. 
It can be purchased through university bookstores or online from 

One initial activity in developing such tools for control has been made by 
Wilson J. Rugh at Johns Hopkins University, who has created a series of inter- 
active demonstrations of basic concepts of control that can be executed over the 
web.^ Modules include Fourier analysis, convolution, the sampling theorem, and 
elementary control systems. One of the most sophisticated tools demonstrates ro- 
bust stabilization, including the ability to specify an uncertainty weight by moving 
poles and zeros of the weighting transfer function with the mouse. A controller can 


4.5. Other Opportunities and Trends 87 

then be designed by dragging the compensator poles and zeros to achieve robust, 
closed loop stability. 


One of the success stories of control is the wide availability of commercial software 
for modeling, analyzing, designing, and implementing control systems. The Controls 
Toolbox in MATLAB provides the basic tools of classical and modern control and 
many other toolboxes are available for more implementing more specialized theory. 
These toolboxes are used throughout academia, government, and industry and give 
students, researchers, and practitioners access to powerful tools that have been 
carefully designed and tested. 

Despite the impressive current state of the art, much of this software is re- 
stricted to a very small class of the systems typically encountered in control and 
there are many gaps that will need to be filled to enable the types of applications 
described in the previous chapter. One area where substantial progress has been 
made recently is in modeling tools, where there are several software packages avail- 
able for modeling, simulation, and analysis of large-scale, complex systems. One 
such is example is ModelicaJ which provides an object oriented language for de- 
scribing complex physical systems. Modelica is particularly noteworthy because it 
was designed to model systems with algebraic constraints, allowing a much richer 
class of systems to be represented. 

Additional tools are needed for control-oriented modeling, analysis, and syn- 
thesis of nonlinear and hybrid systems, particularly those that have a strong inter- 
action with information rich systems, where good scaling properties are required. 
As yet, there is not a standard representational framework for such systems (beyond 
symbolic representations) and hence software tools for nonlinear or hybrid analysis 
are much less used than those for linear systems. One of the main issues here is 
to capture the relevant dynamics in a framework that is amenable to computation. 
Analysis and synthesis must be able to handle systems containing table lookups, 
logical elements, time delays, and models for computation and communication ele- 

The payoff for investing in the development of such tools is clear: it brings 
the advanced theoretical techniques that are developed within the community to 
the people who can most use those results. 

Interaction with Industry and Government 

Interaction with industry is an important component of any engineering research 
or educational activity. The control community has a strong history of impact on 
many important problems and industry involvement will be critical for the even- 
tual success of the future directions described in this report. This could occur 
through cooperative Ph.D. programs where industrial researchers are supported 
half by companies and half by universities to pursue Ph.D.'s (full-time), with the 

'http: //www. modelica. org 

Chapter 4. Education and Outreach 

benefits of bringing more understanding of real-world problems to the university 
and transferring the latest developments back to industry. In addition, industry 
leaders and executives from the control community should continue to interact with 
the community and help communicate the needs of their constituencies. 

The NSF/CSS workshop also recognized the important role that industry plays 
and recommended that educators and funding organizations 

encourage the development of WWW-based initiatives for technical in- 
formation dissemination to industrial users of control systems and en- 
courage the transfer of practical industrial experience to the classroom [1] . 

The further recommended that cooperative efforts between academia and industry, 
especially in terms of educational matters, be significantly expanded. 

The International Federation of Automatic Control (IFAC) is creating a col- 
lection of IFAC Professional Briefs. These Professional Briefs are aimed at a read- 
ership of general professional control engineers (industrial and academic), rather 
than specialist researchers. The briefs provide an introduction and overview of a 
"hot topic," illustrative results, and a sketch of the underlying theory, with special 
attention given to providing information sources such as useful Internet sites, books, 
papers, etc. Eight titles have been selected to launch the Professional Briefs series: 

Computer Controlled Systems 

PID Auto- Tuning 

Control of Biotechnological Processes 

Control Busses and Standards 

Physical-Based Modeling of Mechatronic Systems 

Genetic Algorithms in Control Systems Engineering 

Low Cost Automation in Manufacturing 

Engineering Dependable Industrial Real-Time Software. 

Another avenue for interaction with industry is through the national labora- 
tories. In the United States, many government laboratories have summer faculty 
programs and student internships. Extended visits serve not only to transfer ideas 
and technology from research to application, but also provide a mechanism for un- 
derstanding problem areas of importance to the government and the military. The 
U. S. Air Force Research Laboratory has been particularly active in bringing in 
visitors from universities and provides an example of successful interchange of this 

Finally, there are many opportunities for control researchers to participate in 
government service. This can range from serving on review committees and advisory 
boards to serving as a program manager at a funding agency. Active participation 
by the control community is essential for building understanding and support of the 
role of control. 

Chapter 5 


Control continues to be a field rich in opportunities. In order to realize these 
opportunities, it is important that the next generation of control researchers receive 
the support required to develop new tools and techniques, explore new application 
areas, and reach out to new audiences. Toward this end, the Panel developed five 
major recommendations for accelerating the impact of control. 

5.1 Integrated Control, Computation, 

Inexpensive and ubiquitous sensing, communications, and computation will be a 
major enabler for new applications of control to large-scale, complex systems. Re- 
search in control over networks, control of networks, and design of safety critical, 
large-scale interconnected systems will generate many new research issues and theo- 
retical challenges. A key feature of these systems is their robust yet fragile behavior, 
with cascade failures leading to large disruptions in performance. 

A significant challenge will be to bring together the diverse research communi- 
ties in control, computer science, and communications in order to build the unified 
theory required to make progress in this area. Joint research by these communi- 
ties will be much more team-based and will likely involve groups of domain experts 
working on common problems, in addition to individual investigator-based projects. 

To realize the opportunities in this area, the Panel recommends that govern- 
ment agencies and the control community 

Substantially increase research aimed at the integration of con- 
trol, computer science, communications, and networking. 

In the United States, the Department of Defense has already made substantial 
investment in this area through the Multidisciplinary University Research Initiative 
(MURI) program and this trend should be continued. It will be important to 
create larger, multidisciplinary centers that join control, computer science, and 


90 Chapter 5. Recommendations 

communications and to train engineers and researchers who are knowledgeable in 
these areas. 

Industry involvement will be critical for the eventual success of this integrated 
effort and universities should begin to seek partnerships with relevant companies. 
Examples include manufacturers of air traffic control hardware and software, and 
manufacturers of networking equipment. 

The benefits of increased research in integrated control, communications, and 
computing will be seen in our transportation systems (air, automotive, and rail), our 
communications networks (wired, wireless, and cellular), and enterprise- wide oper- 
ations and supply networks (electrical power, manufacturing, service and repair). 

5.2 Control of Complex Decision Systems 

The role of logic and decision making in control systems is becoming an increas- 
ingly large portion of modern control systems. This decision making includes not 
only traditional logical branching based on system conditions, but higher levels of 
abstract reasoning using high level languages. These problems have traditionally 
been in the domain of the artificial intelligence (AI) community, but the increasing 
role of dynamics, robustness, and interconnection in many applications points to a 
clear need for participation by the control community as well. 

A parallel trend is the use of control in very large scale systems, such as 
logistics and supply chains for entire enterprises. These systems involve decision 
making for very large, very heterogeneous systems where new protocols are required 
for determining resource allocations in the face of an uncertain future. Although 
models will be central to analyzing and designing such systems, these models (and 
the subsequent control mechanisms) must be scalable to very large systems, with 
millions of elements that are themselves as complicated as the systems we currently 
control on a routine basis. 

To tackle these problems, the Panel recommends that government agencies 
and the control community 

Substantially increase research in control at higher levels of 
decision making, moving tow^ard enterprise level systems. 

The extension of control beyond its traditional roots in differential equations is 
an area that the control community has been involved in for many years and it 
is clear that some new ideas are needed. Effective frameworks for analyzing and 
designing systems of this form have not yet been fully developed and the control 
community must get involved in this class of applications in order to understand 
how to formulate the problem. 

A useful technic^ue may be the development of experimental testbeds to explore 
new ideas. In the military arena, these testbeds could consist of collections of 
unmanned vehicles (air, land, sea and space), operating in conjunction with human 
partners and adversaries. In the commercial sector, service robots and personal 
assistants may be a fruitful area for exploration. And in a university setting, the 
emergence of robotic competitions is an interesting trend that control researchers 

5.3. High-Risk, Long-Range Applications of Control 91 

should explore as a mechanism for developing new paradigms and tools. In all of 
these cases, stronger links with the AI community should be explored, since that 
community is currently at the forefront of many of these applications. 

The benefits of research in this area include replacing ad hoc design methods 
by systematic techniques to develop much more reliable and maintainable decision 
systems. It will also lead to more efficient and autonomous enterprise-wide systems 
and, in the military domain, provide new alternatives for defense that minimize the 
risk of human life. 

5.3 High-Risk, Long-Range Applications of Control 

The potential application areas for control are increasing rapidly as advances in 
science and technology develop new understanding of the importance of feedback, 
and new sensors and actuators allow manipulation of heretofore unimagined detail. 
To discover and exploit opportunities in these new domains, experts in control must 
actively participate in new areas of research outside of their traditional roots. At 
the same time, mechanisms must be put in place to educate domain experts about 
control, to allow a fuller dialog, and to accelerate the uses of control across the 
enormous number of possible applications. 

In addition, many applications will require new paradigms for thinking about 
control. For example, the traditional notions of signals that encode information 
through amplitude and phase relationships may need to be extended to allow the 
study of systems where pulse trains or biochemical "signals" are used to trace 

One of the opportunities in many of these domains is to export (and expand) 
the framework for systems-oriented modeling that has been developed in control. 
The tools that have been developed for aggregation and hierarchical modeling can 
be important in many systems where complex phenomena must be understood. 
The tools in control are among the most sophisticated available, particularly with 
respect to uncertainty management. 

To realize some of these opportunities, the Panel recommends that government 
agencies and the control community 

Explore high-risk, long-range applications of control to new^ 
domains such as nanotechnology, quantum mechanics, electro- 
magnetics, biology, and environmental science. 

A challenge in exploring new areas is that experts in two (or more) fields must come 
together, which is often difficult under mainly discipline-based funding constructs. 
There are a variety of mechanisms that might be used to do this, including dual 
investigator funding through control programs that pay for biologists, physicists, 
and others to work on problems side-by-side with control researchers. Similarly, 
funding agencies should broaden the funding of science and technology to include 
funding of the control community through domain-specific programs. 

Another need is to establish "meeting places" where control researchers can 
join with new communities and each can develop an understanding of the principles 
and tools of the other. This could include focused workshops of a week or more to 

92 Chapter 5. Recommendations 

explore control applications in new domains or 4-6 week short courses on control 
that are tuned to a specific applications area, with tutorials in that application area 
as well. 

At universities, new materials are needed to teach non-experts who want to 
learn about control. Universities should also consider dual appointments between 
science and engineering departments that recognize the broad nature of control 
and the need for control to not be confined to a single disciplinary area. Cross- 
disciplinary centers (such as the CCEC at UC Santa Barbara) and programs in 
control (such as the CDS program at Caltech) are natural locations for joint ap- 
pointments and can act as a catalyst for getting into new areas of control by at- 
tracting funding and students outside of traditional disciplines. 

There are many areas ripe for the application of control and increased activity 
in new domains will accelerate the use of control and enable new advances and 
insights. In many of these new application areas, the systems approach championed 
by the control community has yet to be applied, but it will be required for eventual 
engineering applications. Perhaps more important, control has the opportunity to 
revolutionize other fields, especially those where the systems are complicated and 
difficult to understand. Of course, these problems are extremely hard and many 
previous attempts have not always been successful, but the opportunities are great 
and we must continue to strive to move forward. 

5.4 Support for Theory and Interaction with 

A core strength of control has been its respect for and effective use of theory, as well 
as contributions to mathematics driven by control problems. Rigor is a trademark 
of the community and one that has been key to many of its successes. Continued 
interaction with mathematics and support for theory is even more important as the 
applications for control become more complex and more diverse. 

An ongoing need is making the existing knowledge base more compact so that 
the field can continue to grow. Integrating previous results and providing a more 
unified structure for understanding and applying those results is necessary in any 
field and has happened many times in the history of control. This process must 
be continuously pursued and requires steady support for theoreticians working on 
solidifying the foundations of control. Control experts also need to expand the 
applications base by having the appropriate level of abstraction to identify new 
applications of existing theory. 

To ensure the continued health of the field, the Panel recommends that the 
community and funding agencies 

Maintain support for theory and interaction with mathematics, 
broadly interpreted. 

Some possible areas of interaction include dynamical systems, graph theory, combi- 
natorics, complexity theory, queuing theory, statistics, etc. Additional perspectives 
on the interaction of control and mathematics can be found in a recent survey article 

5.5. New Approaches to Education and Outreach 93 

by Brockett [11]. 

A key need is to identify and provide funding mechanisms for people to work 
on core theory. The proliferation of multi-disciplinary, multi-university programs 
have supported many worthwhile projects, but they potentially threaten the base 
of individual investigators who are working on the theory that is required for future 
success. It is important to leave room for theorists on these applications-oriented 
projects and to better articulate the successes of the past so that support for the 
theory is appreciated. Program managers should support a balanced portfolio of 
applications, computation, and theory, with clear articulation of the importance of 
long term, theoretical results. 

The linkage of control with mathematics should also be increased, perhaps 
through new centers and programs. Funding agencies should consider funding na- 
tional institutes for control science that would engage the mathematics community, 
and existing institutes in mathematics should be encouraged to sponsor year-long 
programs on control, dynamics, and systems. 

The benefits of this investment in theory will be a systematic design method- 
ology for building complex systems and rigorous training for the next generation of 
researchers and engineers. 

5.5 New Approaches to Education and Outreach 

As many of the recommendations above indicate, applications of control are expand- 
ing and this is placing new demands on education. The community must continue 
to unify and compact the knowledge base by integrating materials and frameworks 
from the past 40 years. As important, material must be made more accessible to 
a broad range of potential users, well beyond the traditional base of engineering 
science students and practitioners. This includes new uses of control by computer 
scientists, biologists, physicists, and medical researchers. The technical background 
of these constituencies is often very different than traditional engineering disciplines 
and will require new approaches to education. 

The Panel believes that control principles are now a required part of any edu- 
cated scientist's or engineer's background and we recommend that the community 
and funding agencies 

Invest in new^ approaches to education and outreach for the 
dissemination of control concepts and tools to non-traditional 

As a first step toward implementing this recommendation, new courses and text- 
books should be developed for both experts and non-experts. Control should also 
be made a required part of engineering and science curricula at major universities, 
including not only mechanical, electrical, chemical, and aerospace engineering, but 
also computer science, applied physics, and bioengineering. It is also important 
that these courses emphasize the principles of control rather than simply providing 
tools that can be used in a given domain. 

An important element of education and outreach is the continued use of ex- 
periments and the development of new laboratories and software tools. These are 

94 Chapter 5. Recommendations 

much easier to do than ever before and also more important. Laboratories and 
software tools should be integrated into the curriculum, including moving beyond 
their current use in introductory control courses to increased use in advanced (grad- 
uate) course work. The importance of software cannot be overemphasized, both in 
terms of design tools (e. g., MATLAB toolboxes) and implementation (real-time 
algorithms) . 

Increased interaction with industry in education is another important step. 
This could occur through cooperative Ph.D. programs where industrial researchers 
are supported half by companies and half by universities to pursue Ph.D.'s (full- 
time) , with the benefits of bringing more understanding of real- world problems to 
the university and transferring the latest developments back to industry. In addi- 
tion, industry leaders and executives from the control community should continue 
to interact with the community and help communicate the needs of their constituen- 

Additional steps to be taken include the development of new teaching materials 
that can be used to broadly educate the public about control. This might include 
chapters on control in high school textbooks in biology, mathematics, and physics 
or a multimedia CDROM that describes the history, principles, successes, and tools 
for control. Popular books that explain the principles of feedback, or perhaps a 
"cartoon book" on control should be considered. The upcoming IFAC Professional 
Briefs for use in industry are also an important avenue for education. 

The benefits of reaching out to broader communities will be an increased 
awareness of the usefulness of control, and acceleration of the benefits of control 
through broader use of its principles and tools. The use of rigorous design princi- 
ples will result in safer systems, shorter development times, and more transparent 
understanding of key systems issues. 

5.6 Concluding Remarks 

The field of control has a rich history and a strong record of success and impact 
in commercial, military, and scientific applications. The tradition of rigorous use 
of mathematics combined with strong interaction with applications has produced a 
set of tools that are used in a wide variety of technologies. The opportunities for 
future impact are even richer than those of the past, and the field is well positioned 
to expand its tools to apply to new areas and applications. 

The pervasiveness of communications, computing and sensing will enable many 
new applications of control but will also require a substantial expansion of the 
current theory and tools. The control community must embrace new, information 
rich applications and generalize existing concepts to apply to systems at higher 
levels of decisions making. Combined with new, long-range areas that are opening 
up to control techniques, the next decade promises to be a fruitful one for the field. 

The payoffs for investment in control research are substantial. They include 
the successful development of systems that operate reliably, efficiently, and robustly; 
new materials and devices that are made possible through advanced control of man- 
ufacturing processes; and increased understanding of physical and biological systems 

5.6. Concluding Remarks 95 

through the use of control principles. Perhaps most important is the continued de- 
velopment of individuals who embrace a systems perspective and provide technical 
leadership in modeling, analysis, design and testing of complex engineering systems. 


Chapter 5. Recommendations 

Appendix A 

NSF/CSS Workshop on 
New Directions in Control 
Engineering Education 

The National Science Foundation (NSF) and the IEEE Control Systems Society 
(CSS) held a workshop in October 1998 to identify the future needs in control sys- 
tems education [1]. The executive summary of the report is presented here. The full 
report is available from the CDS Panel homepage. 

Executive Summary 

The field of control systems science and engineering is entering a golden age of un- 
precedented growth and opportunity that will likely dwarf the advancements stim- 
ulated by the space program of the 1960s. These opportunities for growth are being 
spurred by enormous advances in computer technology, material science, sensor and 
actuator technology, as well as in the theoretical foundations of dynamical systems 
and control. Many of the opportunities for future growth are at the boundaries 
of traditional disciplines, particularly at the boundary of computer science with 
other engineering disciplines. Control systems technology is the cornerstone of the 
new automation revolution occurring in such diverse areas as household appliances, 
consumer electronics, automotive and aerospace systems, manufacturing systems, 
chemical processes, civil and environmental systems, transportation systems, and 
even biological, economic, and medical systems. 

The needs of industry for well trained control systems scientists and engineers 
are changing, due to marketplace pressures and advances in technology. Future 
generations of engineering students will have to be broadly educated to cope with 
cross-disciplinary applications and rapidly changing technology. At the same time, 
the backgrounds of students are changing. Many come from nontraditional back- 
grounds; they often are less well prepared in mathematics and the sciences while 
being better prepared to work with modern computing technologies. The time is 
thus ripe for major renovations in control and systems engineering education. 

To address these emerging challenges and opportunities, the IEEE Control 
Systems Society initiated the idea of holding a workshop that would bring together 
leading control systems researchers to identify the future needs in control systems 


98 Appendix A. NSF/CSS Workshop on Education 

education. The workshop was held on the campus of the University of lUinois at 
Urbana-Champaign, October 2-3, 1998. It attracted sixty-eight participants. 

This report summarizes the major conclusions and recommendations that 
emerged from the workshop. A slightly modified version of the main body of this 
report will also appear in the October, 1999, issue of the IEEE Control Systems 
Magazine. These recommendations, which cover a broad spectrum of educational 
issues, are addressed to several constituencies, including the National Science Foun- 
dation, control systems professional organizations, and control systems researchers 
and educators. 

1. General Recommendation 

1 Enhance cooperation among various control organizations and control disci- 
plines throughout the world to give attention to control systems education is- 
sues and to increase the general awareness of the importance of control systems 
technology in society. 

Mechanisms to accomplish this include joint sponsorship of conferences, workshops, 
conference sessions, and publications devoted to control systems education as well as 
the development of books, websites, videotapes, and so on, devoted to the promotion 
of control systems technology. 

2. Additional Recommendations 

2 Provide practical experience in control systems engineering to freshmen to 
stimulate future interest and to introduce fundamental notions like feedback 
and the systems approach to engineering. 

This can be accomplished by incorporating modules and/or projects that involve 
principles of control systems into freshmen courses that already exist in many en- 
gineering schools and colleges. 

3 Encourage the development of new courses and course materials that will sig- 
nificantly broaden the standard first introductory control systems course at the 
undergraduate level. 

Such new courses would be accessible to all third year engineering students and 
would deal with fundamental principles of system modeling, planning, design, opti- 
mization, hardware and software implementation, computer aided control systems 
design and simulation, and systems performance evaluation. Equally important, 
such courses would stress the fundamental applications and importance of feedback 
control as well as the limits of feedback, and would provide a bridge between control 
systems engineering and other branches of engineering that benefit from systems 
engineering concepts such as networks and communications, biomedical engineering, 
computer science, economics, etc. 

4 Develop follow on courses at the undergraduate level that provide the neces- 
sary breadth and depth to prepare students both for industrial careers and for 
graduate studies in systems and control. 


Advanced courses in both traditional control methodologies, like digital control, and 
courses treating innovative control applications should be available to undergradu- 
ate students in order to convey the excitement of control systems engineering while 
still providing the fundamentals needed in practice. 

5 Promote control systems laboratory development, especially the concept of 
shared laboratories, and make experimental projects an integral part of control 
education for all students, including graduate students. 

Mechanisms to accomplish this include increased support for the development of 
hands-on control systems laboratories, as well as the development of benchmark 
control systems examples that are accessible via the Internet. Shared laboratories 
within individual colleges or universities as well as shared laboratories among differ- 
ent universities makes more efficient use of resources, increases exposure of students 
to the multidisciplinary nature of control, and promotes the interaction of faculty 
and students across disciplines. 

The promotion of laboratory development also includes mechanisms for con- 
tinued support. Too often, laboratories are developed and then abandoned after a 
few years because faculty do not have time or funds for continued support. It is 
equally important, therefore, to provide continuity of support for periodic hardware 
and software upgrades, maintenance, and the development of new experiments. 

The National Science Foundation and IEEE Control Systems Society can also 
help realize this goal by developing workshops and short courses for laboratory 
development and instruction to promote interaction and sharing of laboratory de- 
velopment experiences among faculty from different universities. 

6 Emphasize the integration of control systems education and research at all 
levels of instruction. 

The National Science Foundation program. Research Experiences for Undergradu- 
ates, exemplifies an excellent mechanism to accomplish this at the undergraduate 
level and should be continued. Sponsorship of student competitions in control is 
another such mechanism that should be encouraged. At the graduate level control 
educators should take advantage of National Science Foundation programs such as 
the Integrative Graduate Education and Research Training Program (IGERT) and 
the Course, Curriculum, and Laboratory Improvement Program (CCLI). 

7 Improve information exchange by developing a centralized Internet repository 
for educational materials. 

These materials should include tutorials, exercises, case studies, examples, and his- 
tories, as well as laboratory exercises, software, manuals, etc. The IEEE Control 
Systems Society can play a leadership role in the development of such a repository 
by coordinating the efforts among various public and private agencies. 

8 Promote the development of a set of standards for Internet based control sys- 
tems materials and identify pricing mechanisms to provide financial compen- 
sation to Internet laboratory providers and educational materials providers. 

100 Appendix A. NSF/CSS Workshop on Education 

A mechanism to accomplish this could be a National Science Foundation sponsored 
workshop devoted to Internet standards for control education materials and pricing. 

9 Develop WWW-based peer reviewed electronic journal on control education 
and laboratory development. 

Control systems professional organizations can play leadership roles, perhaps work- 
ing with the American Society of Engineering Education (ASEE) to accomplish this 

10 Encourage the development of initiatives for technical information dissemi- 
nation to industrial users of control systems and encourage the transfer of 
practical industrial experience to the classroom. 

Mechanisms to accomplish this include special issues of journals and magazines de- 
voted to industrial applications of control, programs to bring speakers from industry 
to the classroom, and programs that allow university faculty to spend extended pe- 
riods of time in industry. 


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